Symmetric merge mode motion vector coding

ABSTRACT

Systems, devices, and methods are described herein for symmetric merge mode motion vector coding. Symmetric bi-prediction (bi-pred) motion vectors (MVs) may be constructed from available candidates in a merge candidate list for regular inter prediction merge mode and/or affine prediction merge mode. Available MV merge candidates may be symmetrically extended or mapped in either direction (e.g., between reference pictures before and after a current picture), for example, when coding a picture that allows bi-directional motion compensation prediction (MCP). A symmetric bi-pred merge candidate may be selected among merge candidates for predicting the motion information of a current prediction unit (PU). The symmetric mapping construction may be repeated by a decoder (e.g., based on a coded index of the MV merge candidate list), for example, to obtain the same merge candidates and coded MV at an encoder.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/816,586, filed on Mar. 11, 2019, and entitled “Symmetric MotionVector Difference Coding,” the entirety of which is incorporated byreference as if fully set forth herein.

BACKGROUND

Video coding systems may be used to compress digital video signals,e.g., to reduce the storage and/or transmission bandwidth needed forsuch signals. Video coding systems may include block-based,wavelet-based, and/or object-based systems.

SUMMARY

Systems, devices, and methods are described herein for symmetric mergemode motion vector coding. Symmetric bi-prediction (bi-pred) motionvectors (MVs) may be constructed from available candidates in a mergecandidate list for regular inter prediction merge mode and/or affineprediction merge mode. Available MV merge candidates may besymmetrically extended or mapped in either direction (e.g., betweenreference pictures before and after a current picture), for example,when coding a picture that allows bi-directional motion compensationprediction (MCP). A symmetric bi-pred MV may be selected among mergecandidates as an MV for a current prediction unit (PU). The symmetricmapping construction may be repeated (e.g., based on a coded index ofthe MV merge candidate list), for example, at a decoding device toobtain the same merge candidates and coded MV at an encoding device.

In an example, a method may be implemented to determine the motioninformation, including a motion vector (MV) for a prediction unit (PU)in a current picture. The method may be implemented, for example, by adevice, which may comprise a computer readable storage medium storing,and/or a processor configured to execute, computer executableinstructions that, when executed, perform the method to determine amotion vector (MV) for a prediction unit (PU) in a current picture. Amethod may include obtaining (e.g., retrieving, generating orconstructing) a merge candidate list for a PU in a current picture. Themerge candidate list may include a first merge candidate comprising afirst MV associated with a first reference picture in a first referencepicture list. A symmetric merge candidate may be obtained (e.g.,constructed), for example, via symmetric mapping of the first mergecandidate. The symmetric merge candidate may comprise a second MVsymmetric to the first MV. The symmetric merge candidate may beassociated with a second reference picture in a second reference picturelist. The symmetric merge candidate may be a bi-prediction mergecandidate and may include the first MV associated with a first referencepicture in the first reference picture list, and the second MV symmetricto the first MV associated with the second reference picture in thesecond reference picture list. The symmetric merge candidate may bemerged (e.g., added) to the merge candidate list. A merge candidate(e.g., the symmetric merge candidate) may be selected from the mergecandidate list for predicting an MV the PU.

The first and second reference pictures may be symmetric relative to thecurrent picture. For example, the first and second reference picturesmay have a same picture order count (POC) distance, e.g. in oppositedirections, to the current picture.

Construction of a symmetric merge candidate may be based on whether thesecond reference picture list contains a symmetric reference picture ofthe first reference picture, e.g., with a picture order count (POC)distance to the current picture equal to a POC distance between thefirst reference picture to the current picture. In an example, on acondition that a symmetric reference picture exists in the secondreference picture list, the first merge candidate may be selected toderive the symmetric merge candidate. The symmetric reference picture isthe second reference picture of the symmetric merge candidate.

The other reference picture list may not include a symmetric referencepicture. A reference picture with a closest POC distance may be selectedas the second reference picture of the symmetric merge candidate. Forexample, a reference picture in the second reference picture list with aPOC distance to the current picture closest to the POC distance betweenthe first reference picture and the current picture may be selected.

Motion vector scaling may be applied, for example, to construct thesecond MV for the symmetric merge candidate. Scaling may be based on thePOC distance between the second reference picture and the currentpicture, and the PO distance between the first reference picture and thecurrent picture.

In an example, a symmetric merge candidate may be constructed and addedto the merge candidate list, for example, only if the first mergecandidate is a uni-directional prediction merge candidate. In variousimplementations, the symmetric merge candidate may be constructed andadded to the merge candidate list, for example, regardless of whetherthe first merge candidate is a bi-directional prediction (bi-pred) mergecandidate or a uni-prediction merge candidate.

For example, a determination may be made that the first merge candidateis a bi-directional prediction candidate with the first MV based on thefirst reference picture in the first reference picture list and a thirdMV based on a third reference picture in the second reference picturelist. A second symmetric merge candidate may be constructed viasymmetric mapping of the first merge candidate, the second symmetricmerge candidate may comprise a fourth MV symmetric to the third MV andassociated with a fourth reference picture in the first referencepicture list. A POC distance between the fourth reference picture andthe current picture may be equal or similar to a POC distance betweenthe third reference picture and the current picture.

Addition of a symmetric merge candidate to a merge candidate list may bebased on a determination, e.g., before adding the symmetric mergecandidate to the merge candidate list, that the symmetric mergecandidate is not redundant with any other merge candidate in the mergecandidate list.

Addition of a symmetric merge candidate to a merge candidate list may bebased on a determination, e.g., before adding the symmetric mergecandidate to the merge candidate list, that adding the merge candidateto the merge candidate list will not exceed at least one of a maximumnumber of allowed merge candidates and a maximum number of allowedsymmetric merge candidates.

A symmetric merge candidate may be added to a merge candidate list in aspecific order, e.g., after non-zero MV merge candidates and before anyzero MV merge candidates in the merge candidate list.

The merge candidates may be regular or affine merge candidates. Forexample, the first merge candidate may include at least two candidatecontrol point MVs (CPMVs), and the symmetric merge candidate maycomprise at least two symmetric-mapped CPMVs, respectively symmetric tothe at least two CPMVs of the first merge candidate. The at least twosymmetric-mapped CPMVs may be derived by a symmetric mapping of the atleast two CPMVs.

In an example of a four-parameter affine model, there may be a symmetricmapping of first and second candidate CPMVs of the first merge candidateto first and second symmetric CPMVs of the symmetric merge candidate.For example, four affine candidate CPMV parameters comprising x and yspatial translations, a zooming factor and a rotation angle may besymmetrically mapped to four affine symmetric CPMV parameters comprisingnegative x and y spatial translations, an inverse zooming factor and anegative rotation angle. The first and second symmetric CPMVs may bederived based on the four affine symmetric CPMV parameters.

In an example of a six-parameter affine model, there may be a symmetricmapping of first, second, and third candidate CPMVs of the first mergecandidate to first, second, and third symmetric CPMVs of the symmetricmerge candidate. For example, six affine candidate CPMV parameterscomprising x and y spatial translations, x and y zooming factors and xand y rotation angles may be symmetrically mapped to six affinesymmetric CPMV parameters comprising negative x and y spatialtranslations, inverse x and y zooming factors and negative x and yrotation angles. The first, second, and third symmetric CPMVs may bederived based on the six affine symmetric CPMV parameters.

The method(s) described herein may be performed by a decoder. In someexamples, the method(s) herein or a corresponding method(s) may beperformed by an encoder. A computer-readable medium may includeinstructions for causing one or more processors to perform the method(s)described herein. A computer program product including instructionswhich, when the program is executed by one or more processors, may causethe one or more processors to carry out the method(s) described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a system diagram illustrating an example communicationssystem in which one or more disclosed embodiments may be implemented.

FIG. 1B is a system diagram illustrating an example wirelesstransmit/receive unit (WTRU) that may be used within the communicationssystem illustrated in FIG. 1A according to an embodiment.

FIG. 1C is a system diagram illustrating an example radio access network(RAN) and an example core network (CN) that may be used within thecommunications system illustrated in FIG. 1A according to an embodiment.

FIG. 1D is a system diagram illustrating a further example RAN and afurther example ON that may be used within the communications systemillustrated in FIG. 1A according to an embodiment.

FIG. 2 is a diagram showing an example block-based video encoder.

FIG. 3 is a diagram showing an example video decoder.

FIG. 4 is a diagram showing an example of a system in which variousaspects and examples may be implemented.

FIG. 5 is a diagram showing example positions of spatial mergecandidates.

FIG. 6 is a diagram showing an example of motion vector scaling for atemporal merge candidate.

FIGS. 1A-C are diagrams showing examples of control point based affinemotion models including a 4 parameter affine model, a 6 parameter affinemodel, and an affine motion vector field per sub-block.

FIGS. 8A and 8B are diagrams showing example locations of inheritedaffine motion predictors and examples of control point motion vectorinheritance.

FIG. 9 is a diagram showing example locations of candidate positions forconstructed affine merge mode.

FIG. 10 is a diagram showing an example of decoder side motion vectorrefinement.

FIGS. 11A and 11B are diagrams showing examples of symmetric merge MVcandidate construction for regular and affine motion.

DETAILED DESCRIPTION

A detailed description of illustrative embodiments will now be describedwith reference to the various Figures. Although this descriptionprovides a detailed example of possible implementations, it should benoted that the details are intended to be exemplary and in no way limitthe scope of the application.

FIG. 1A is a diagram illustrating an example communications system 100in which one or more disclosed embodiments may be implemented. Thecommunications system 100 may be a multiple access system that providescontent, such as voice, data, video, messaging, broadcast, etc., tomultiple wireless users. The communications system 100 may enablemultiple wireless users to access such content through the sharing ofsystem resources, including wireless bandwidth. For example, thecommunications systems 100 may employ one or more channel accessmethods, such as code division multiple access (CDMA), time divisionmultiple access (TDMA), frequency division multiple access (FDMA),orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tailunique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM(UW-OFDM), resource block-filtered OFDM, filter bank multicarrier(FBMC), and the like.

As shown in FIG. 1A, the communications system 100 may include wirelesstransmit/receive units (WTRUs) 102 a, 102 b, 102 c, 102 d, a RAN104/113, a CN 106/115, a public switched telephone network (PSTN) 108,the Internet 110, and other networks 112, though it will be appreciatedthat the disclosed embodiments contemplate any number of WTRUs, basestations, networks, and/or network elements. Each of the WVTRUs 102 a,102 b, 102 c, 102 d may be any type of device configured to operateand/or communicate in a wireless environment. By way of example, theWTRUs 102 a, 102 b, 102 c, 102 d, any of which may be referred to as a“station” and/or a “STA”, may be configured to transmit and/or receivewireless signals and may include a user equipment (UE), a mobilestation, a fixed or mobile subscriber unit, a subscription-based unit, apager, a cellular telephone, a personal digital assistant (PDA), asmartphone, a laptop, a netbook, a personal computer, a wireless sensor,a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watchor other wearable, a head-mounted display (HMD), a vehicle, a drone, amedical device and applications (e.g., remote surgery), an industrialdevice and applications (e.g., a robot and/or other wireless devicesoperating in an industrial and/or an automated processing chaincontexts), a consumer electronics device, a device operating oncommercial and/or industrial wireless networks, and the like. Any of theWTRUs 102 a, 102 b, 102 c and 102 d may be interchangeably referred toas a UE.

The communications systems 100 may also include a base station 114 aand/or a base station 114 b. Each of the base stations 114 a, 114 b maybe any type of device configured to wirelessly interface with at leastone of the WTRUs 102 a, 102 b, 102 c, 102 d to facilitate access to oneor more communication networks, such as the CN 106/115, the Internet110, and/or the other networks 112. By way of example, the base stations114 a, 114 b may be a base transceiver station (BTS), a Node-B, an eNodeB, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller,an access point (AP), a wireless router, and the like. While the basestations 114 a, 114 b are each depicted as a single element, it will beappreciated that the base stations 114 a, 114 b may include any numberof interconnected base stations and/or network elements.

The base station 114 a may be part of the RAN 104/113, which may alsoinclude other base stations and/or network elements (not shown), such asa base station controller (BSC), a radio network controller (RNC), relaynodes, etc. The base station 114 a and/or the base station 114 b may beconfigured to transmit and/or receive wireless signals on one or morecarrier frequencies, which may be referred to as a cell (not shown).These frequencies may be in licensed spectrum, unlicensed spectrum, or acombination of licensed and unlicensed spectrum. A cell may providecoverage for a wireless service to a specific geographical area that maybe relatively fixed or that may change over time. The cell may furtherbe divided into cell sectors. For example, the cell associated with thebase station 114 a may be divided into three sectors. Thus, in oneembodiment, the base station 114 a may include three transceivers, i.e.,one for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and mayutilize multiple transceivers for each sector of the cell. For example,beamforming may be used to transmit and/or receive signals in desiredspatial directions.

The base stations 114 a, 114 b may communicate with one or more of theWTRUs 102 a, 102 b, 102 c, 102 d over an air interface 116, which may beany suitable wireless communication link (e.g., radio frequency (RF),microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet(UV), visible light, etc.). The air interface 116 may be establishedusing any suitable radio access technology (RAT).

More specifically, as noted above, the communications system 100 may bea multiple access system and may employ one or more channel accessschemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. Forexample, the base station 114 a in the RAN 104/113 and the WTRUs 102 a,102 b, 102 c may implement a radio technology such as Universal MobileTelecommunications System (UMTS) Terrestrial Radio Access (UTRA), whichmay establish the air interface 115/116/117 using wideband CDMA (WCDMA).WCDMA may include communication protocols such as High-Speed PacketAccess (HSPA) and/or Evolved HSPA (HSPA+). HSPA may include High-SpeedDownlink (DL) Packet Access (HSDPA) and/or High-Speed UL Packet Access(HSUPA).

In an embodiment, the base station 114 a and the WTRUs 102 a, 102 b, 102c may implement a radio technology such as Evolved UMTS TerrestrialRadio Access (E-UTRA), which may establish the air interface 116 usingLong Term Evolution (LTE) and/or LTE-Advanced (LTE-A) and/orLTE-Advanced Pro (LTE-A Pro).

In an embodiment, the base station 114 a and the WTRUs 102 a, 102 b, 102c may implement a radio technology such as NR Radio Access, which mayestablish the air interface 116 using New Radio (NR).

In an embodiment, the base station 114 a and the WTRUs 102 a, 102 b, 102c may implement multiple radio access technologies. For example, thebase station 114 a and the WVTRUs 102 a, 102 b, 102 c may implement LTEradio access and NR radio access together, for instance using dualconnectivity (DC) principles. Thus, the air interface utilized by WTRUs102 a, 102 b, 102 c may be characterized by multiple types of radioaccess technologies and/or transmissions sent to/from multiple types ofbase stations (e.g., a eNB and a gNB).

In other embodiments, the base station 114 a and the WTRUs 102 a, 102 b,102 c may implement radio technologies such as IEEE 802.11 (i.e.,Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperabilityfor Microwave Access (WiMAX)), CDMA2000, CDMA2000 1×, CDMA2000 EV-DO,Interim Standard 2000 (IS-2000), interim Standard 95 (IS-95), interimStandard 856 (IS-856), Global System for Mobile communications (GSM),Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and thelike.

The base station 114 b in FIG. 1A may be a wireless router, Home Node B,Home eNode B, or access point, for example, and may utilize any suitableRAT for facilitating wireless connectivity in a localized area, such asa place of business, a home, a vehicle, a campus, an industrialfacility, an air corridor (e.g., for use by drones), a roadway, and thelike. In one embodiment, the base station 114 b and the WTRUs 102 c, 102d may implement a radio technology such as IEEE 802.11 to establish awireless local area network (WLAN). In an embodiment, the base station114 b and the WTRUs 102 c, 102 d may implement a radio technology suchas IEEE 802.15 to establish a wireless personal area network (WPAN). Inyet another embodiment, the base station 114 b and the WTRUs 102 c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE,LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. Asshown in FIG. 1A, the base station 114 b may have a direct connection tothe Internet 110. Thus, the base station 114 b may not be required toaccess the Internet 110 via the ON 106/115.

The RAN 104/113 may be in communication with the CN 106/115, which maybe any type of network configured to provide voice, data, applications,and/or voice over internet protocol (VoIP) services to one or more ofthe WTRUs 102 a, 102 b, 102 c, 102 d. The data may have varying qualityof service (QoS) requirements, such as differing throughputrequirements, latency requirements, error tolerance requirements,reliability requirements, data throughput requirements, mobilityrequirements, and the like. The CN 106/115 may provide call control,billing services, mobile location-based services, pre-paid calling,Internet connectivity, video distribution, etc., and/or performhigh-level security functions, such as user authentication. Although notshown in FIG. 1A, it will be appreciated that the RAN 104/113 and/or theCN 106/115 may be in direct or indirect communication with other RANsthat employ the same RAT as the RAN 104/113 or a different RAT. Forexample, in addition to being connected to the RAN 104/113, which may beutilizing a NR radio technology, the ON 106/115 may also be incommunication with another RAN (not shown) employing a GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or WiFi radio technology.

The CN 106/115 may also serve as a gateway for the WTRUs 102 a, 102 b,102 c, 102 d to access the PSTN 108, the Internet 110, and/or the othernetworks 112. The PSTN 108 may include circuit-switched telephonenetworks that provide plain old telephone service (POTS). The Internet110 may include a global system of interconnected computer networks anddevices that use common communication protocols, such as thetransmission control protocol (TCP), user datagram protocol (UDP) and/orthe internet protocol (IP) in the TCP/IP internet protocol suite. Thenetworks 112 may include wired and/or wireless communications networksowned and/or operated by other service providers. For example, thenetworks 112 may include another CN connected to one or more RANs, whichmay employ the same RAT as the RAN 104/113 or a different RAT.

Some or all of the WTRUs 102 a, 102 b, 102 c, 102 d in thecommunications system 100 may include multi-mode capabilities (e.g., theWTRUs 102 a, 102 b, 102 c, 102 d may include multiple transceivers forcommunicating with different wireless networks over different wirelesslinks). For example, the WTRU 102 c shown in FIG. 1A may be configuredto communicate with the base station 114 a, which may employ acellular-based radio technology, and with the base station 114 b, whichmay employ an IEEE 802 radio technology.

FIG. 1B is a system diagram illustrating an example WTRU 102. As shownin FIG. 1B, the WTRU 102 may include a processor 118, a transceiver 120,a transmit/receive element 122, a speaker/microphone 124, a keypad 126,a display/touchpad 128, non-removable memory 130, removable memory 132,a power source 134, a global positioning system (GPS) chipset 136,and/or other peripherals 138, among others. It will be appreciated thatthe WTRU 102 may include any sub-combination of the foregoing elementswhile remaining consistent with an embodiment.

The processor 118 may be a general purpose processor, a special purposeprocessor, a conventional processor, a digital signal processor (DSP), aplurality of microprocessors, one or more microprocessors in associationwith a DSP core, a controller, a microcontroller, Application SpecificIntegrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)circuits, any other type of integrated circuit (IC), a state machine,and the like. The processor 118 may perform signal coding, dataprocessing, power control, input/output processing, and/or any otherfunctionality that enables the WTRU 102 to operate in a wirelessenvironment. The processor 118 may be coupled to the transceiver 120,which may be coupled to the transmit/receive element 122, While FIG. 1Bdepicts the processor 118 and the transceiver 120 as separatecomponents, it will be appreciated that the processor 118 and thetransceiver 120 may be integrated together in an electronic package orchip.

The transmit/receive element 122 may be configured to transmit signalsto, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, thetransmit/receive element 122 may be an antenna configured to transmitand/or receive RF signals. In an embodiment, the transmit/receiveelement 122 may be an emitter/detector configured to transmit and/orreceive IR, UV, or visible light signals, for example. In yet anotherembodiment, the transmit/receive element 122 may be configured totransmit and/or receive both RF and light signals. It will beappreciated that the transmit/receive element 122 may be configured totransmit and/or receive any combination of wireless signals.

Although the transmit/receive element 122 is depicted in FIG. 1E as asingle element, the WTRU 102 may include any number of transmit/receiveelements 122. More specifically, the WTRU 102 may employ MIMOtechnology. Thus, in one embodiment, the WTRU 102 may include two ormore transmit/receive elements 122 (e.g., multiple antennas) fortransmitting and receiving wireless signals over the air interface 116.

The transceiver 120 may be configured to modulate the signals that areto be transmitted by the transmit/receive element 122 and to demodulatethe signals that are received by the transmit/receive element 122. Asnoted above, the WTRU 102 may have multi-mode capabilities. Thus, thetransceiver 120 may include multiple transceivers for enabling the WTRU102 to communicate via multiple RATs, such as NR and IEEE 802.11, forexample.

The processor 118 of the WTRU 102 may be coupled to, and may receiveuser input data from, the speaker/microphone 124, the keypad 126, and/orthe display/touchpad 128 (e.g., a liquid crystal display (LCD) displayunit or organic light-emitting diode (OLED) display unit). The processor118 may also output user data to the speaker/microphone 124, the keypad126, and/or the display/touchpad 128. In addition, the processor 118 mayaccess information from, and store data in, any type of suitable memory,such as the non-removable memory 130 and/or the removable memory 132.The non-removable memory 130 may include random-access memory (RAM),read-only memory (ROM), a hard disk, or any other type of memory storagedevice. The removable memory 132 may include a subscriber identitymodule (SIM) card, a memory stick, a secure digital (SD) memory card,and the like. In other embodiments, the processor 118 may accessinformation from, and store data in, memory that is not physicallylocated on the WTRU 102, such as on a server or a home computer (notshown).

The processor 118 may receive power from the power source 134, and maybe configured to distribute and/or control the power to the othercomponents in the WTRU 102. The power source 134 may be any suitabledevice for powering the WTRU 102. For example, the power source 134 mayinclude one or more dry cell batteries (e.g., nickel-cadmium (NiCd),nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion),etc.), solar cells, fuel cells, and the like.

The processor 118 may also be coupled to the GPS chipset 136, which maybe configured to provide location information (e.g., longitude andlatitude) regarding the current location of the WTRU 102. In additionto, or in lieu of, the information from the GPS chipset 136, the WTRU102 may receive location information over the air interface 116 from abase station (e.g., base stations 114 a, 114 b) and/or determine itslocation based on the timing of the signals being received from two ormore nearby base stations. It will be appreciated that the WTRU 102 mayacquire location information by way of any suitablelocation-determination method while remaining consistent with anembodiment.

The processor 118 may further be coupled to other peripherals 138, whichmay include one or more software and/or hardware modules that provideadditional features, functionality and/or wired or wirelessconnectivity. For example, the peripherals 138 may include anaccelerometer, an e-compass, a satellite transceiver, a digital camera(for photographs and/or video), a universal serial bus (USB) port, avibration device, a television transceiver, a hands free headset, aBluetooth® module, a frequency modulated (FM) radio unit, a digitalmusic player, a media player, a video game player module, an Internetbrowser, a Virtual Reality and/or Augmented Reality (VR/AR) device, anactivity tracker, and the like. The peripherals 138 may include one ormore sensors, the sensors may be one or more of a gyroscope, anaccelerometer, a hall effect sensor, a magnetometer, an orientationsensor, a proximity sensor, a temperature sensor, a time sensor; ageolocation sensor; an altimeter, a light sensor, a touch sensor, amagnetometer, a barometer, a gesture sensor, a biometric sensor, and/ora humidity sensor.

The WTRU 102 may include a full duplex radio for which transmission andreception of some or all of the signals (e.g., associated withparticular subframes for both the UL (e.g., for transmission) anddownlink (e.g., for reception) may be concurrent and/or simultaneous.The full duplex radio may include an interference management unit toreduce and or substantially eliminate self-interference via eitherhardware (e.g., a choke) or signal processing via a processor (e.g., aseparate processor (not shown) or via processor 118). In an embodiment,the WRTU 102 may include a half-duplex radio for which transmission andreception of some or all of the signals (e.g., associated withparticular subframes for either the UL (e.g., for transmission) or thedownlink (e.g., for reception)).

FIG. 1C is a system diagram illustrating the RAN 104 and the CN 106according to an embodiment. As noted above, the RAN 104 may employ anE-UTRA radio technology to communicate with the WTRUs 102 a, 102 b, 102c over the air interface 116. The RAN 104 may also be in communicationwith the ON 106.

The RAN 104 may include eNode-Bs 160 a, 160 b, 160 c, though it will beappreciated that the RAN 104 may include any number of eNode-Bs whileremaining consistent with an embodiment. The eNode-Bs 160 a, 160 b, 160c may each include one or more transceivers for communicating with theWTRUs 102 a, 102 b, 102 c over the air interface 116. In one embodiment,the eNode-Bs 160 a, 160 b, 160 c may implement MIMO technology. Thus,the eNode-B 160 a, for example, may use multiple antennas to transmitwireless signals to, and/or receive wireless signals from, the WTRU 102a.

Each of the eNode-Bs 160 a, 160 b, 160 c may be associated with aparticular cell (not shown) and may be configured to handle radioresource management decisions, handover decisions, scheduling of usersin the UL and/or DL, and the like. As shown in FIG. 1C, the eNode-Bs 160a, 160 b, 160 c may communicate with one another over an X2 interface.

The CN 106 shown in FIG. 10 may include a mobility management entity(MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN)gateway (or PGW) 166. While each of the foregoing elements are depictedas part of the CN 106, it will be appreciated that any of these elementsmay be owned and/or operated by an entity other than the CN operator.

The MME 162 may be connected to each of the eNode-Bs 162 a, 162 b, 162 cin the RAN 104 via an S1 interface and may serve as a control node. Forexample, the MME 162 may be responsible for authenticating users of theWTRUs 102 a, 102 b, 102 c, bearer activation/deactivation, selecting aparticular serving gateway during an initial attach of the WTRUs 102 a,102 b, 102 c, and the like. The MME 162 may provide a control planefunction for switching between the RAN 104 and other RANs (not shown)that employ other radio technologies, such as GSM and/or WCDMA.

The SGW 164 may be connected to each of the eNode Bs 160 a, 160 b, 160 cin the RAN 104 via the S1 interface. The SGW 164 may generally route andforward user data packets to/from the WTRUs 102 a, 102 b, 102 c. The SGW164 may perform other functions, such as anchoring user planes duringinter-eNode B handovers, triggering paging when DL data is available forthe WTRUs 102 a, 102 b, 102 c, managing and storing contexts of theWTRUs 102 a, 102 b, 102 c, and the like.

The SGW 164 may be connected to the PGW 166, which may provide the WTRUs102 a, 102 b, 102 c with access to packet-switched networks, such as theInternet 110, to facilitate communications between the WTRUs 102 a, 102b, 102 c and P-enabled devices.

The ON 106 may facilitate communications with other networks. Forexample, the CN 106 may provide the WTRUs 102 a, 102 b, 102 c withaccess to circuit-switched networks, such as the PSTN 108, to facilitatecommunications between the WTRUs 102 a, 102 b, 102 c and traditionalland-line communications devices. For example, the CN 106 may include,or may communicate with, an IP gateway (e.g., an IP multimedia subsystem(IMS) server) that serves as an interface between the ON 106 and thePSTN 108. In addition, the CN 106 may provide the WTRUs 102 a, 102 b,102 c with access to the other networks 112, which may include otherwired and/or wireless networks that are owned and/or operated by otherservice providers.

Although the WTRU is described in FIGS. 1A-1D as a wireless terminal, itis contemplated that in certain representative embodiments that such aterminal may use (e.g., temporarily or permanently) wired communicationinterfaces with the communication network.

In representative embodiments, the other network 112 may be a WLAN.

A WLAN in Infrastructure Basic Service Set (BSS) mode may have an AccessPoint (AP) for the BSS and one or more stations (STAs) associated withthe AP. The AP may have an access or an interface to a DistributionSystem (DS) or another type of wired/wireless network that carriestraffic in to and/or out of the BSS. Traffic to STAs that originatesfrom outside the BSS may arrive through the AP and may be delivered tothe STAs. Traffic originating from STAs to destinations outside the BSSmay be sent to the AP to be delivered to respective destinations.Traffic between STAs within the BSS may be sent through the AR, forexample, where the source STA may send traffic to the AP and the AP maydeliver the traffic to the destination STA. The traffic between STAswithin a BSS may be considered and/or referred to as peer-to-peertraffic. The peer-to-peer traffic may be sent between (e.g., directlybetween) the source and destination STAs with a direct link setup (DLS).In certain representative embodiments, the DLS may use an 802.11e DLS oran 802.11z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS)mode may not have an AR, and the STAs (e.g., all of the STAs) within orusing the IBSS may communicate directly with each other. The IBSS modeof communication may sometimes be referred to herein as an “ad-hoc” modeof communication.

When using the 802.11ac infrastructure mode of operation or a similarmode of operations, the AP may transmit a beacon on a fixed channel,such as a primary channel. The primary channel may be a fixed width(e.g., 20 MHz wide bandwidth) or a dynamically set width via signaling.The primary channel may be the operating channel of the BSS and may beused by the STAs to establish a connection with the AP. In certainrepresentative embodiments, Carrier Sense Multiple Access with CollisionAvoidance (CSMA/CA) may be implemented, for example in in 802.11systems. For CSMA/CA, the STAs (e.g., every STA), including the AP, maysense the primary channel. If the primary channel is sensed/detectedand/or determined to be busy by a particular STA, the particular STA mayback off. One STA (e.g. only one station) may transmit at any given timein a given BSS.

High Throughput (HT) STAs may use a 40 MHz wide channel forcommunication, for example, via a combination of the primary 20 MHzchannel with an adjacent or nonadjacent 20 MHz channel to form a 40 MHzwide channel.

Very High Throughput (VHT) STAs may support 20 MHz, 40 MHz, 80 MHz,and/or 160 MHz wide channels. The 40 MHz, and/or 80 MHz, channels may beformed by combining contiguous 20 MHz channels. A 160 MHz channel may beformed by combining 8 contiguous 20 MHz channels, or by combining twonon-contiguous 80 MHz channels, which may be referred to as an 80+80configuration. For the 80+80 configuration, the data, after channelencoding, may be passed through a segment parser that may divide thedata into two streams, inverse Fast Fourier Transform (IFFT) processing,and time domain processing, may be done on each stream separately. Thestreams may be mapped on to the two 80 MHz channels, and the data may betransmitted by a transmitting STA. At the receiver of the receiving STA,the above described operation for the 80+80 configuration may bereversed, and the combined data may be sent to the Medium Access Control(MAC).

Sub 1 GHz modes of operation are supported by 802.11af and 802.11ah. Thechannel operating bandwidths, and carriers, are reduced in 802.11af and802.11ah relative to those used in 802.11n, and 802.11 ac. 802.11sfsupports 5 MHz, 10 MHz and 20 MHz bandwidths in the TV White Space(TVWS) spectrum, and 802.11ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and16 MHz bandwidths using non-TVWS spectrum. According to a representativeembodiment, 802.11ah may support Meter Type Control/Machine-TypeCommunications, such as MTC devices in a macro coverage area. MTCdevices may have certain capabilities, for example, limited capabilitiesincluding support for (e.g., only support for) certain and/or limitedbandwidths. The MTC devices may include a battery with a battery lifeabove a threshold (e.g., to maintain a very long battery life).

WLAN systems, which may support multiple channels, and channelbandwidths, such as 802.11n, 802.11ac, 802.11af, and 802.11ah, include achannel which may be designated as the primary channel. The primarychannel may have a bandwidth equal to the largest common operatingbandwidth supported by all STAs in the BSS. The bandwidth of the primarychannel may be set and/or limited by a STA, from among all STAs inoperating in a BSS, which supports the smallest bandwidth operatingmode. In the example of 802.11ah, the primary channel may be 1 MHz widefor STAs (e.g., MTC type devices) that support (e.g., only support) a 1MHz mode, even if the AP, and other STAs in the BSS support 2 MHz, 4MHz, 8 MHz, 16 MHz, and/or other channel bandwidth operating modes.Carrier sensing and/or Network Allocation Vector (NAV) settings maydepend on the status of the primary channel. If the primary channel isbusy, for example, due to a STA (which supports only a 1 MHz operatingmode), transmitting to the AP, the entire available frequency bands maybe considered busy even though a majority of the frequency bands remainsidle and may be available.

In the United States, the available frequency bands, which may be usedby 802.11ah, are from 902 MHz to 928 MHz. In Korea, the availablefrequency bands are from 917.5 MHz to 923.5 MHz. In Japan, the availablefrequency bands are from 916.5 MHz to 927.5 MHz. The total bandwidthavailable for 802.11 ah is 6 MHz to 26 MHz depending on the countrycode.

FIG. 1D is a system diagram illustrating the RAN 113 and the CN 115according to an embodiment. As noted above, the RAN 113 may employ an NRradio technology to communicate with the WTRUs 102 a, 102 b, 102 c overthe air interface 116. The RAN 113 may also be in communication with theCN 115.

The RAN 113 may include gNBs 180 a, 180 b, 180 c, though it will beappreciated that the RAN 113 may include any number of gNBs whileremaining consistent with an embodiment. The gNBs 180 a, 180 b, 180 cmay each include one or more transceivers for communicating with theWTRUs 102 a, 102 b, 102 c over the air interface 116. In one embodiment,the gNBs 180 a, 180 b, 180 c may implement MIMO technology. For example,gNBs 180 a, 108 b may utilize beamforming to transmit signals to and/orreceive signals from the gNBs 180 a, 180 b, 180 c. Thus, the gNB 180 a,for example, may use multiple antennas to transmit wireless signals to,and/or receive wireless signals from, the WTRU 102 a: In an embodiment,the gNBs 180 a, 180 b, 180 c may implement carrier aggregationtechnology. For example, the gNB 180 a may transmit multiple componentcarriers to the WTRU 102 a (not shown). A subset of these componentcarriers may be on unlicensed spectrum while the remaining componentcarriers may be on licensed spectrum. In an embodiment, the gNBs 180 a,180 b, 180 c may implement Coordinated Multi-Point (CoMP) technology.For example, WTRU 102 a may receive coordinated transmissions from gNB180 a and gNB 180 b (and/or gNB 180 c).

The WTRUs 102 a, 102 b, 102 c may communicate with gNBs 180 a, 180 b,180 c using transmissions associated with a scalable numerology. Forexample, the OFDM symbol spacing and/or OFDM subcarrier spacing may varyfor different transmissions, different cells, and/or different portionsof the wireless transmission spectrum. The WTRUs 102 a, 102 b, 102 c maycommunicate with gNBs 180 a, 180 b, 180 c using subframe or transmissiontime intervals (TTIs) of various or scalable lengths (e.g., containingvarying number of OFDM symbols and/or lasting varying lengths ofabsolute time).

The gNBs 180 a, 180 b, 180 c may be configured to communicate with theWTRUs 102 a, 102 b, 102 c in a standalone configuration and/or anon-standalone configuration. In the standalone configuration, WTRUs 102a, 102 b, 102 c may communicate with gNBs 180 a, 180 b, 180 c withoutalso accessing other RANs (e.g., such as eNode-Bs 160 a, 160 b, 160 c).In the standalone configuration, WTRUs 102 a, 102 b, 102 c may utilizeone or more of gNBs 180 a, 180 b, 180 c as a mobility anchor point. Inthe standalone configuration, WTRUs 102 a, 102 b, 102 c may communicatewith gNBs 180 a, 180 b, 180 c using signals in an unlicensed band: In anon-standalone configuration WTRUs 102 a, 102 b, 102 c may communicatewith/connect to gNBs 180 a, 180 b, 180 c while also communicatingwith/connecting to another RAN such as eNode-Bs 160 a, 160 b, 160 c. Forexample, WTRUs 102 a, 102 b, 102 c may implement DC principles tocommunicate with one or more gNBs 180 a, 180 b, 180 c and one or moreeNode-Bs 160 a, 160 b, 160 c substantially simultaneously. In thenon-standalone configuration, eNode-Bs 160 a, 160 b, 160 c may serve asa mobility anchor for WTRUs 102 a, 102 b, 102 c and gNBs 180 a, 180 b,180 c may provide additional coverage and/or throughput for servicingWTRUs 102 a, 102 b, 102 c.

Each of the gNBs 180 a, 180 b, 180 c may be associated with a particularcell (not shown) and may be configured to handle radio resourcemanagement decisions, handover decisions, scheduling of users in the ULand/or DL, support of network slicing, dual connectivity, interworkingbetween NR and E-UTRA, routing of user plane data towards User PlaneFunction (UPF) 184 a, 184 b, routing of control plane informationtowards Access and Mobility Management Function (AMF) 182 a, 182 b andthe like. As shown in FIG. 1D, the gNBs 180 a, 180 b, 180 c maycommunicate with one another over an Xn interface.

The CN 115 shown in FIG. 1D may include at least one AMF 182 a, 182 b,atleast one UPF 184 a,184 b, at least one Session Management Function(SMF) 183 a, 183 b, and possibly a Data Network (DN) 185 a, 185 b. Whileeach of the foregoing elements are depicted as part of the CN 115, itwill be appreciated that any of these elements may be owned and/oroperated by an entity other than the CN operator.

The AMF 182 a, 182 b may be connected to one or more of the gNBs 180 a,180 b, 180 c in the RAN 113 via an N2 interface and may serve as acontrol node. For example, the AMF 182 a, 182 b may be responsible forauthenticating users of the WTRUs 102 a, 102 b, 102 c, support fornetwork slicing (e.g., handling of different PDU sessions with differentrequirements), selecting a particular SMF 183 a, 183 b, management ofthe registration area, termination of NAS signaling, mobilitymanagement, and the like. Network slicing may be used by the AMF 182 a,182 b in order to customize CN support for WTRUs 102 a, 102 b, 102 cbased on the types of services being utilized WTRUs 102 a, 102 b, 102 c.For example, different network slices may be established for differentuse cases such as services relying on ultra-reliable low latency (URLLC)access, services relying on enhanced massive mobile broadband (eMBB)access, services for machine type communication (MTC) access, and/or thelike. The AMF 162 may provide a control plane function for switchingbetween the RAN 113 and other RANs (not shown) that employ other radiotechnologies, such as LTE, LTE-A, LTE-A Pro, and/or non-3GPP accesstechnologies such as WiFi.

The SMF 183 a, 183 b may be connected to an AMF 182 a, 182 b in the CN115 via an N11 interface. The SMF 183 a, 183 b may also be connected toa UPF 184 a, 184 b in the CN 115 via an N4 interface. The SMF 183 a, 183b may select and control the UPF 184 a, 184 b and configure the routingof traffic through the UPF 184 a, 184 b. The SMF 183 a, 183 b mayperform other functions, such as managing and allocating UE IP address,managing PDU sessions, controlling policy enforcement and QoS, providingdownlink data notifications, and the like. A PDU session type may beP-based, non-IP based, Ethernet-based, and the like.

The UPF 184 a, 184 b may be connected to one or more of the gNBs 180 a,180 b, 180 c in the RAN 113 via an N3 interface, which may provide theWTRUs 102 a, 102 b, 102 c with access to packet-switched networks, suchas the internet 110, to facilitate communications between the WTRUs 102a, 102 b, 102 c and IP-enabled devices. The UPF 184, 184 b may performother functions, such as routing and forwarding packets, enforcing userplane policies, supporting multi-homed PDU sessions, handling user planeQoS, buffering downlink packets, providing mobility anchoring, and thelike.

The CN 115 may facilitate communications with other networks. Forexample, the CN 115 may include, or may communicate with, an IP gateway(e.g., an IP multimedia subsystem (IMS) server) that serves as aninterface between the CN 115 and the PSTN 108. In addition, the CN 115may provide the WTRUs 102 a, 102 b, 102 c with access to the othernetworks 112, which may include other wired and/or wireless networksthat are owned and/or operated by other service providers. In oneembodiment, the WTRUs 102 a, 102 b, 102 c may be connected to a localData Network (DN) 185 a, 185 b through the UPF 184 a, 184 b via the N3interface to the UPF 184 a, 184 b and an N6 interface between the UPF184 a, 184 b and the DN 185 a, 185 b.

In view of FIGS. 1A-1D, and the corresponding description of FIGS.1A-1D, one or more, or all, of the functions described herein withregard to one or more of: WTRU 102 a-d, Base Station 114 a-b, eNode-B 16ta-c, MME 162, SGW 164, POW 166, gNB 180 a-c, AMF 182 a-b, UPF 184 a-b,SMF 183 a-b, DN 185 a-b, and/or any other device(s) described herein,may be performed by one or more emulation devices (not shown). Theemulation devices may be one or more devices configured to emulate oneor more, or all, of the functions described herein. For example, theemulation devices may be used to test other devices and/or to simulatenetwork and/or WTRU functions.

The emulation devices may be designed to implement one or more tests ofother devices in a lab environment and/or in an operator networkenvironment. For example, the one or more emulation devices may performthe one or more, or all, functions while being fully or partiallyimplemented and/or deployed as part of a wired and/or wirelesscommunication network in order to test other devices within thecommunication network. The one or more emulation devices may perform theone or more, or all, functions while being temporarilyimplemented/deployed as part of a wired and/or wireless communicationnetwork. The emulation device may be directly coupled to another devicefor purposes of testing and/or may performing testing using over-the-airwireless communications.

The one or more emulation devices may perform the one or more, includingall, functions while not being implemented/deployed as part of a wiredand/or wireless communication network. For example, the emulationdevices may be utilized in a testing scenario in a testing laboratoryand/or a non-deployed (e.g., testing) wired and/or wirelesscommunication network in order to implement testing of one or morecomponents. The one or more emulation devices may be test equipment.Direct RF coupling and/or wireless communications via RF circuitry(e.g., which may include one or more antennas) may be used by theemulation devices to transmit and/or receive data.

This application describes a variety of aspects, including tools,features, examples, models, approaches, etc. Many of these aspects aredescribed with specificity and, at least to show the individualcharacteristics, are often described in a manner that may soundlimiting. However, this is for purposes of clarity in description, anddoes not limit the application or scope of those aspects. Indeed, all ofthe different aspects may be combined and interchanged to providefurther aspects. Moreover, the aspects may be combined and interchangedwith aspects described in earlier filings as well.

The aspects described and contemplated in this application may beimplemented in many different forms. FIGS. 5-11 described herein mayprovide some examples, but other examples are contemplated. Thediscussion of FIGS. 5-11 does not limit the breadth of theimplementations. At least one of the aspects generally relates to videoencoding and decoding, and at least one other aspect generally relatesto transmitting a bitstream generated or encoded. These and otheraspects may be implemented as a method, an apparatus, a computerreadable storage medium having stored thereon instructions for encodingor decoding video data according to any of the methods described, and/ora computer readable storage medium having stored thereon a bitstreamgenerated according to any of the methods described.

In the present application, the terms “reconstructed” and “decoded” maybe used interchangeably, the terms “pixel” and “sample” may be usedinterchangeably, the terms “image,” “picture” and “frame” may be usedinterchangeably.

Various methods are described herein, and each of the methods comprisesone or more steps or actions for achieving the described method. Unlessa specific order of steps or actions is required for proper operation ofthe method, the order and/or use of specific steps and/or actions may bemodified or combined. Additionally, terms such as “first”, “second”,etc. may be used in various examples to modify an element, component,step, operation, etc., such as, for example, a “first decoding” and a“second decoding”. Use of such terms does not imply an ordering to themodified operations unless specifically required. So, in this example,the first decoding need not be performed before the second decoding, andmay occur, for example, before, during, or in an overlapping time periodwith the second decoding.

Various methods and other aspects described in this application may beused to modify modules, for example, decoding modules, of a videoencoder 100 and decoder 200 as shown in FIG. 2 and FIG. 3. Moreover, thesubject matter disclosed herein may be applied, for example, to anytype, format or version of video coding, whether described in a standardor a recommendation, whether pre-existing or future-developed, andextensions of any such standards and recommendations. Unless indicatedotherwise, or technically precluded, the aspects described in thisapplication may be used individually or in combination.

Various numeric values are used in examples described the presentapplication, such as a maximum allowed size of a merge list as 6, a 4×4luma sub-block, a 4×4 chroma sub-block, a 1/16 fraction accuracy, a4-parameter affine model, a 6-parameter affine model, etc. These andother specific values are for purposes of describing examples and theaspects described are not limited to these specific values.

FIG. 2 is a diagram showing an example video encoder. Variations ofexample encoder 200 are contemplated, but the encoder 200 is describedbelow for purposes of clarity without describing all expectedvariations.

Before being encoded, the video sequence may go through pre-encodingprocessing (201), for example, applying a color transform to the inputcolor picture (e.g., conversion from RGB 4:4:4 to YCbCr 4:2:0), orperforming a remapping of the input picture components in order to get asignal distribution more resilient to compression (for instance using ahistogram equalization of one of the color components). Metadata may beassociated with the pre-processing, and attached to the bitstream.

In the encoder 200, a picture is encoded by the encoder elements asdescribed below. The picture to be encoded is partitioned (202) andprocessed in units of, for example, coding units (CUs). Each unit isencoded using, for example, either an intra or inter mode. When a unitis encoded in an intra mode, it performs intra prediction (260). In aninter mode, motion estimation (275) and compensation (270) areperformed. The encoder decides (205) which one of the intra mode orinter mode to use for encoding the unit, and indicates the intra/interdecision by, for example, a prediction mode flag. Prediction residualsare calculated, for example, by subtracting (210) the predicted blockfrom the original image block.

The prediction residuals are then transformed (225) and quantized (230).The quantized transform coefficients, as well as motion vectors andother syntax elements, are entropy coded (245) to output a bitstream.The encoder can skip the transform and apply quantization directly tothe non-transformed residual signal. The encoder can bypass bothtransform and quantization, i.e., the residual is coded directly withoutthe application of the transform or quantization processes.

The encoder decodes an encoded block to provide a reference for furtherpredictions. The quantized transform coefficients are de-quantized (240)and inverse transformed (250) to decode prediction residuals, Combining(255) the decoded prediction residuals and the predicted block, an imageblock is reconstructed. In-loop filters (265) are applied to thereconstructed picture to perform, for example, deblocking/SAO (SampleAdaptive Offset) filtering to reduce encoding artifacts. The filteredimage is stored at a reference picture buffer (280),

FIG. 3 is a diagram showing an example of a video decoder. In exampledecoder 300, a bitstream is decoded by the decoder elements as describedbelow. Video decoder 300 generally performs a decoding pass reciprocalto the encoding pass as described in FIG. 2. The encoder 200 alsogenerally performs video decoding as part of encoding video data.

In particular, the input of the decoder includes a video bitstream,which may be generated by video encoder 200. The bitstream is firstentropy decoded (330) to obtain transform coefficients, motion vectors,and other coded information. The picture partition information indicateshow the picture is partitioned. The decoder may therefore divide (335)the picture according to the decoded picture partitioning information.The transform coefficients are de-quantized (340) and inversetransformed (350) to decode the prediction residuals. Combining (355)the decoded prediction residuals and the predicted block, an image blockis reconstructed. The predicted block may be obtained (370) from intraprediction (360) or motion-compensated prediction (i.e., interprediction) (375), In-loop filters (365) are applied to thereconstructed image. The filtered image is stored at a reference picturebuffer (380).

The decoded picture can further go through post-decoding processing(385), for example, an inverse color transform (e.g. conversion fromYCbCr 4:2:0 to RGB 4:4:4) or an inverse remapping performing the inverseof the remapping process performed in the pre-encoding processing (201).The post-decoding processing can use metadata derived in thepre-encoding processing and signaled in the bitstream.

FIG. 4 is a diagram showing an example of a system in which variousaspects and examples described herein may be implemented. System 400 maybe embodied as a device including the various components described belowand is configured to perform one or more of the aspects described inthis document. Examples of such devices, include, but are not limitedto, various electronic devices such as personal computers, laptopcomputers, smartphones, tablet computers, digital multimedia set topboxes, digital television receivers, personal video recording systems,connected home appliances, and servers. Elements of system 400, singlyor in combination, may be embodied in a single integrated circuit (IC),multiple ICs, and/or discrete components. For example, in at least oneexample, the processing and encoder/decoder elements of system 400 aredistributed across multiple ICs and/or discrete components. In variousexamples, the system 400 is communicatively coupled to one or more othersystems, or other electronic devices, via, for example, a communicationsbus or through dedicated input and/or output ports. In various examples,the system 400 is configured to implement one or more of the aspectsdescribed in this document.

The system 400 includes at least one processor 410 configured to executeinstructions loaded therein for implementing, for example, the variousaspects described in this document, Processor 410 can include embeddedmemory, input output interface, and various other circuitries as knownin the art. The system 400 includes at least one memory 420 (e.g., avolatile memory device, and/or a non-volatile memory device). System 400includes a storage device 440, which can include non-volatile memoryand/or volatile memory, including, but not limited to, ElectricallyErasable Programmable Read-Only Memory (EEPROM), Read-Only Memory (ROM),Programmable Read-Only Memory (PROM), Random Access Memory (RAM),Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM),flash, magnetic disk drive, and/or optical disk drive. The storagedevice 440 can include an internal storage device, an attached storagedevice (including detachable and non-detachable storage devices), and/ora network accessible storage device, as non-limiting examples.

System 400 includes an encoder/decoder module 430 configured, forexample, to process data to provide an encoded video or decoded video,and the encoder/decoder module 430 can include its own processor andmemory. The encoder/decoder module 430 represents module(s) that may beincluded in a device to perform the encoding and/or decoding functions.As is known, a device can include one or both of the encoding anddecoding modules. Additionally, encoder/decoder module 430 may beimplemented as a separate element of system 400 or may be incorporatedwithin processor 410 as a combination of hardware and software as knownto those skilled in the art.

Program code to be loaded onto processor 410 or encoder/decoder 430 toperform the various aspects described in this document may be stored instorage device 440 and subsequently loaded onto memory 420 for executionby processor 410. In accordance with various examples, one or more ofprocessor 410, memory 420, storage device 440, and encoder/decodermodule 430 can store one or more of various items during the performanceof the processes described in this document. Such stored items caninclude, but are not limited to, the input video, the decoded video orportions of the decoded video, the bitstream, matrices, variables, andintermediate or final results from the processing of equations,formulas, operations, and operational logic.

In some examples, memory inside of the processor 410 and/or theencoder/decoder module 430 is used to store instructions and to provideworking memory for processing that is needed during encoding ordecoding. In other examples, however, a memory external to theprocessing device (for example, the processing device may be either theprocessor 410 or the encoder/decoder module 430) is used for one or moreof these functions. The external memory may be the memory 420 and/or thestorage device 4140, for example, a dynamic volatile memory and/or anon-volatile flash memory. In several examples, an external non-volatileflash memory is used to store the operating system of, for example, atelevision. In at least one example, a fast external dynamic volatilememory such as a RAM is used as working memory for video coding anddecoding operations.

The input to the elements of system 400 may be provided through variousinput devices as indicated in block 445. Such input devices include, butare not limited to, (i) a radio frequency (RF) portion that receives anRF signal transmitted, for example, over the air by a broadcaster, (ii)a Component (COMP) input terminal (or a set of COMP input terminals),(iii) a Universal Serial Bus (USB) input terminal, and/or (iv) a HighDefinition Multimedia Interface (HDMI) input terminal. Other examples,not shown in FIG. 4, include composite video.

In various examples, the input devices of block 445 have associatedrespective input processing elements as known in the art. For example,the RF portion may be associated with elements suitable for (i)selecting a desired frequency (also referred to as selecting a signal,or band-limiting a signal to a band of frequencies), (ii) downconvertingthe selected signal, (iii) band-limiting again to a narrower band offrequencies to select (for example) a signal frequency band which may bereferred to as a channel in certain examples, (iv) demodulating thedownconverted and band-limited signal, (v) performing error correction,and (vi) demultiplexing to select the desired stream of data packets.The RF portion of various examples includes one or more elements toperform these functions, for example, frequency selectors, signalselectors, band-limiters, channel selectors, filters, downconverters,demodulators, error correctors, and demultiplexers. The RF portion caninclude a tuner that performs various of these functions, including, forexample, downconverting the received signal to a lower frequency (forexample, an intermediate frequency or a near-baseband frequency) or tobaseband. In one set-top box example, the RF portion and its associatedinput processing element receives an RF signal transmitted over a wired(for example, cable) medium, and performs frequency selection byfiltering, downconverting, and filtering again to a desired frequencyband. Various examples rearrange the order of the above-described (andother) elements, remove some of these elements, and/or add otherelements performing similar or different functions. Adding elements caninclude inserting elements in between existing elements, such as, forexample, inserting amplifiers and an analog-to-digital converter. Invarious examples, the RF portion includes an antenna.

Additionally, the USB and/or HDMI terminals can include respectiveinterface processors for connecting system 400 to other electronicdevices across USB and/or HDMI connections. It is to be understood thatvarious aspects of input processing, for example, Reed-Solomon errorcorrection, may be implemented, for example, within a separate inputprocessing IC or within processor 410 as necessary. Similarly, aspectsof USB or HDMI interface processing may be implemented within separateinterface ICs or within processor 410 as necessary. The demodulated,error corrected, and demultiplexed stream is provided to variousprocessing elements, including, for example, processor 410, andencoder/decoder 430 operating in combination with the memory and storageelements to process the datastream as necessary for presentation on anoutput device.

Various elements of system 400 may be provided within an integratedhousing, Within the integrated housing, the various elements may beinterconnected and transmit data therebetween using suitable connectionarrangement 425, for example, an internal bus as known in the art,including the Inter-IC (I2C) bus, wiring, and printed circuit boards.

The system 400 includes communication interface 450 that enablescommunication with other devices via communication channel 460. Thecommunication interface 450 can include, but is not limited to, atransceiver configured to transmit and to receive data overcommunication channel 460. The communication interface 450 can include,but is not limited to, a modem or network card and the communicationchannel 460 may be implemented, for example, within a wired and/or awireless medium.

Data is streamed, or otherwise provided, to the system 400, in variousexamples, using a wireless network such as a Wi-Fi network, for exampleIEEE 802.11 (IEEE refers to the institute of Electrical and ElectronicsEngineers). The Wi-Fi signal of these examples is received over thecommunications channel 460 and the communications interface 450 whichare adapted for Wi-Fi communications. The communications channel 460 ofthese examples is typically connected to an access point or router thatprovides access to external networks including the Internet for allowingstreaming applications and other over-the-top communications. Otherexamples provide streamed data to the system 400 using a set-top boxthat delivers the data over the HDMI connection of the input block 445.Still other examples provide streamed data to the system 400 using theRF connection of the input block 445. As indicated above, variousexamples provide data in a non-streaming manner. Additionally, variousexamples use wireless networks other than Wi-Fi, for example a cellularnetwork or a Bluetooth network.

The system 400 can provide an output signal to various output devices,including a display 475, speakers 485, and other peripheral devices 495.The display 475 of various examples includes one or more of, forexample, a touchscreen display, an organic light-emitting diode (OLED)display, a curved display, and/or a foldable display. The display 475may be for a television, a tablet, a laptop, a cell phone (mobilephone), or other device. The display 475 can also be integrated withother components (for example, as in a smart phone), or separate (forexample, an external monitor for a laptop). The other peripheral devices495 include, in various examples of examples, one or more of astand-alone digital video disc (or digital versatile disc) (DVR, forboth terms), a disk player, a stereo system, and/or a lighting system.Various examples use one or more peripheral devices 495 that provide afunction based on the output of the system 400. For example, a diskplayer performs the function of playing the output of the system 400.

In various examples, control signals are communicated between the system400 and the display 475, speakers 485, or other peripheral devices 495using signaling such as AV.Link, Consumer Electronics Control (CEC), orother communications protocols that enable device-to-device control withor without user intervention. The output devices may be communicativelycoupled to system 400 via dedicated connections through respectiveinterfaces 470, 480, and 490. Alternatively, the output devices may beconnected to system 400 using the communications channel 460 via thecommunications interface 450. The display 475 and speakers 485 may beintegrated in a single unit with the other components of system 400 inan electronic device such as, for example, a television. In variousexamples, the display interface 470 includes a display driver, such as,for example, a timing controller (T Con) chip.

The display 475 and speakers 485 can alternatively be separate from oneor more of the other components, for example, if the RF portion of input445 is part of a separate set-top box. In various examples in which thedisplay 475 and speakers 485 are external components, the output signalmay be provided via dedicated output connections, including, forexample, HDMI ports, USB ports, or COMP outputs.

The examples may be carried out by computer software implemented by theprocessor 410 or by hardware, or by a combination of hardware andsoftware. As a non-limiting example, the examples may be implemented byone or more integrated circuits. The memory 420 may be of any typeappropriate to the technical environment and may be implemented usingany appropriate data storage technology, such as optical memory devices,magnetic memory devices, semiconductor-based memory devices, fixedmemory, and removable memory, as non-limiting examples. The processor410 may be of any type appropriate to the technical environment, and canencompass one or more of microprocessors, general purpose computers,special purpose computers, and processors based on a multi-corearchitecture, as non-limiting examples.

Various implementations involve decoding. “Decoding”, as used in thisapplication, can encompass all or part of the processes performed, forexample, on a received encoded sequence in order to produce a finaloutput suitable for display. In various examples, such processes includeone or more of the processes typically performed by a decoder, forexample, entropy decoding, inverse quantization, inverse transformation,and differential decoding. In various examples, such processes also, oralternatively, include processes performed by a decoder of variousimplementations described in this application, for example, performingsymmetric merge mode MV decoding, generating merge candidate lists,decoding motion vectors (MVs) for motion compensated prediction (MCP),generating symmetric merge candidates for regular and affine interprediction, generating history-based MVP (HMVP) merge candidates,performing an MV refinement search of bi-prediction MVs (e.g.,performing decoder side motion vector refinement (DMVR)), utilizinglook-up-tables (LUTs) to solve for affine model parameter values, and/ordecoding an index of an MVP list for MVP and MV difference (MVD), etc.

As further examples, in one example “decoding” refers only to entropydecoding, in another example “decoding” refers only to differentialdecoding, and in another example “decoding” refers to a combination ofentropy decoding and differential decoding. Whether the phrase “decodingprocess” is intended to refer specifically to a subset of operations orgenerally to the broader decoding process will be clear based on thecontext of the specific descriptions and is believed to be wellunderstood by those skilled in the art.

Various implementations involve encoding. In an analogous way to theabove discussion about “decoding” “encoding” as used in this applicationcan encompass all or part of the processes performed, for example, on aninput video sequence in order to produce an encoded bitstream. Invarious examples, such processes include one or more of the processestypically performed by an encoder, for example, partitioning,differential encoding, transformation, quantization, and entropyencoding. In various examples, such processes also, or alternatively,include processes performed by an encoder of various implementationsdescribed in this application, for example, performing symmetric mergemode MV encoding, generating merge candidate lists, encoding motionvectors (MVs) for motion compensated prediction (MCP), generatingsymmetric merge candidates for regular and affine inter prediction,generating history-based MVP (HMVP) merge candidates, performing an MVrefinement search of bi-prediction MVs), utilizing look-up-tables (LUTs)to solve for affine model parameter values, and/or encoding an index ofan MVP list for MVP and MV difference (MVD), etc.

As further examples, in one example “encoding” refers only to entropyencoding, in another example “encoding” refers only to differentialencoding, and in another example “encoding” refers to a combination ofdifferential encoding and entropy encoding. Whether the phrase “encodingprocess” is intended to refer specifically to a subset of operations orgenerally to the broader encoding process will be clear based on thecontext of the specific descriptions and is believed to be wellunderstood by those skilled in the art.

Note that syntax elements as used herein, for example, coding syntax onmerge mode (e.g., merge_flag and merge_index), coding syntax on affinemerge mode (e.g., merge_subbiock_flag and/or merge_subblock_index),etc., are descriptive terms. As such, they do not preclude the use ofother syntax element names.

When a figure is presented as a flow diagram, it should be understoodthat it also provides a block diagram of a corresponding apparatus.Similarly, when a figure is presented as a block diagram, it should beunderstood that it also provides a flow diagram of a correspondingmethod/process.

Various examples refer to rate distortion optimization. In particular,during the encoding process, the balance or trade-off between the rateand distortion is usually considered, often given the constraints ofcomputational complexity. The rate distortion optimization is usuallyformulated as minimizing a rate distortion function, which is a weightedsum of the rate and of the distortion. There are different approaches tosolve the rate distortion optimization problem. For example, theapproaches may be based on an extensive testing of all encoding options,including all considered modes or coding parameters values, with acomplete evaluation of their coding cost and related distortion of thereconstructed signal after coding and decoding. Faster approaches mayalso be used, to save encoding complexity, in particular withcomputation of an approximated distortion based on the prediction or theprediction residual signal, not the reconstructed one. Mix of these twoapproaches can also be used, such as by using an approximated distortionfor only some of the possible encoding options, and a completedistortion for other encoding options. Other approaches only evaluate asubset of the possible encoding options. More generally, many approachesemploy any of a variety of techniques to perform the optimization, butthe optimization is not necessarily a complete evaluation of both thecoding cost and related distortion.

The implementations and aspects described herein may be implemented in,for example, a method or a process, an apparatus, a software program, adata stream, or a signal. Even if only discussed in the context of asingle form of implementation (for example, discussed only as a method),the implementation of features discussed can also be implemented inother forms (for example, an apparatus or program). An apparatus may beimplemented in, for example, appropriate hardware, software, andfirmware. The methods may be implemented in, for example, a processor,which refers to processing devices in general, including, for example, acomputer, a microprocessor, an integrated circuit, or a programmablelogic device. Processors also include communication devices, such as,for example, computers, cell phones, portable/personal digitalassistants (“PDAs”), and other devices that facilitate communication ofinformation between end-users.

Reference to “one example” or “an example” or “one implementation” or“an implementation”, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the example is included in at least one example. Thus,the appearances of the phrase “in one example” or “in an example” or “inone implementation” or “in an implementation”, as well any othervariations, appearing in various places throughout this application arenot necessarily all referring to the same example.

Additionally, this application may refer to “determining” various piecesof information. Determining the information can include one or more of,for example, estimating the information, calculating the information,predicting the information, or retrieving the information from memory.Obtaining may include receiving, retrieving, constructing, generating,and/or determining.

Further, this application may refer to “accessing” various pieces ofinformation. Accessing the information can include one or more of, forexample, receiving the information, retrieving the information (forexample, from memory), storing the information, moving the information,copying the information, calculating the information, determining theinformation, predicting the information, or estimating the information.

Additionally, this application may refer to “receiving” various piecesof information. Receiving is, as with “accessing”, intended to be abroad term. Receiving the information can include one or more of, forexample, accessing the information, or retrieving the information (forexample, from memory). Further, “receiving” is typically involved, inone way or another, during operations such as, for example, storing theinformation, processing the information, transmitting the information,moving the information, copying the information, erasing theinformation, calculating the information, determining the information,predicting the information, or estimating the information.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “AB”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as is clear to one of ordinary skill inthis and related arts, for as many items as are listed.

Also, as used herein, the word “signal” refers to, among other things,indicating something to a corresponding decoder. Encoder signals mayinclude, for example, a size of a merge candidate list, a coded mergecandidate index of a merge MV prediction candidate list, etc. In thisway, in an example the same parameter is used at both the encoder sideand the decoder side. Thus, for example, an encoder can transmit(explicit signaling) a particular parameter to the decoder so that thedecoder can use the same particular parameter. Conversely, if thedecoder already has the particular parameter as well as others, thensignaling may be used without transmitting (implicit signaling) tosimply allow the decoder to know and select the particular parameter. Byavoiding transmission of any actual functions, a bit savings is realizedin various examples. It is to be appreciated that signaling may beaccomplished in a variety of ways. For example, one or more syntaxelements, flags, and so forth are used to signal information to acorresponding decoder in various examples. While the preceding relatesto the verb form of the word “signal”, the word “signal” can also beused herein as a noun.

As will be evident to one of ordinary skill in the art, implementationsmay produce a variety of signals formatted to carry information that maybe, for example, stored or transmitted. The information can include, forexample, instructions for performing a method, or data produced by oneof the described implementations. For example, a signal may be formattedto carry the bitstream of a described example. Such a signal may beformatted, for example, as an electromagnetic wave (for example, using aradio frequency portion of spectrum) or as a baseband signal. Theformatting may include, for example, encoding a data stream andmodulating a carrier with the encoded data stream. The information thatthe signal carries may be, for example, analog or digital information.The signal may be transmitted over a variety of different wired orwireless links, as is known. The signal may be stored on aprocessor-readable medium.

Many examples are described herein. Features of examples may be providedalone or in any combination, across various claim categories and types.Further, examples may include one or more of the features, devices, oraspects described herein, alone or in any combination, across variousclaim categories and types.

Video coding systems may be used to compress digital video signals,which may reduce the storage needs and/or the transmission bandwidth ofvideo signals. Video coding systems may include block-based,wavelet-based, and/or object-based systems.

A block-based hybrid coding architecture may combine inter-picture andintra-picture prediction and transform coding with entropy coding. Oneor more coding aspects may be implemented (e.g. for coding efficiency),including, for example, one or more of: (i) coding structure, (ii) intraprediction, (iii) inter prediction, transform, quantization andcoefficients coding, (iv) in loop filter, and/or (v) screen contentcoding.

Coding structure may be implemented, for example, with multi-type treeblock partitioning, such as quad-tree, binary tree and ternary treepartitioning.

Intra prediction may be implemented, for example, with 65 angular intraprediction directions, e.g., including one or more of wide angleprediction and/or linear model (LM) chroma mode.

Inter prediction may be implemented, for example, with one or more of anaffine motion model, sub-block temporal motion vector prediction(SbTMVP), adaptive motion vector precision, decoder-side motion vectorrefinement (DMVR), triangular partitions, combined intra and interprediction (CIIP), merge mode with motion vector difference (MMVD),bi-directional optical flow (BDOF), and/or bi-predictive weightedaveraging (BPWA).

Transform, quantization and coefficients coding may be implemented, forexample, with one or more of the following: multiple primary transformselection with discrete cosine transform (DCT)2, discrete sine transform(DST)7 and DCT8, dependent quantization with max quantization parameter(QP) increased from 51 to 63, and/or modified transform coefficientcoding.

In loop filters may be implemented, for example, with a generalizedadaptive loop filter (GALF), Screen content coding may be implemented,for example, with intra Block Copy (IBC). 360-degree video coding may beimplemented, for example, with horizontal wrap-around motioncompensation.

Different MV coding modes (e.g., a merge mode and an advanced motionvector prediction (AMVP) mode) may be implemented, for example, toencode motion vectors (MVs) for motion compensated prediction (MCP). Inmerge mode, coded MVs from neighboring PUs (e.g., spatially and/ortemporally neighboring PUs) may be collected to create a merge MVcandidate list. An index to the list may be coded and/or transmitted tothe decoder. In AMVP mode, MVs candidates from neighboring PUs may beused as MV predictors (MVPs) and additional MV differences (MVDs) may becoded.

Continuity of motion trajectory may be assumed and exploited (e.g., forcoding efficiency), for example, for pictures that allow bi-directionalprediction. Symmetric MVD mapping may, for example, code an MVD forfor-ward prediction and derive an MVD for backward prediction from theforward prediction (e.g., via symmetric mapping). A symmetric MVDmapping may be used (e.g. in DMVR), for example, to perform an MVrefinement search of bi-prediction MVs (e.g., at the decoder side).

Symmetric mapping may be used in merge mode MV coding. Merge mode MVcandidates may be collected or constructed. For example, mergecandidates may include one or more of the following: coded MVs fromspatially neighboring CUs, coded MVs from a temporally collocated CU,constructed MVs from a spatially neighboring CU (e.g., for affine motionmode), MVs from previously coded CUs, pair wise averaged MVs from topcandidates of an existing merge candidate list, constructed symmetricbi-directional prediction (bi-pred) MVs, and zero MVs.

Symmetric bi-prediction (bi-pred) MVs may be constructed from available(e.g., existing) candidates in a merge candidate list. The continuity ofmotion may be leveraged, for example, when coding a picture that allowsfor bi-directional prediction, which involves motion compensatedprediction (MCP) from reference pictures before and after a currentpicture. An available MV merge candidate may be symmetricallyextended/mapped in either direction (e.g., backward or forward betweenreference pictures), for example, when coding a picture allowing forbi-directional prediction. A constructed symmetric bi-pred mergecandidate may be used (e.g. in a merge candidate list) as an MV mergecandidate for an encoder or decoder to choose from. A symmetric bi-predMV candidate may be selected (e.g. to code as an MV for a current PU),for example, if motion continuity holds. A decoder may repeat symmetricmapping construction implemented by an encoder, for example, to obtain acoded MV. A coded index of an MV merge candidate list may be provided toand used by a decoder, for example, to generate a merged candidate listand/or a coded MV. Coding efficiency may be improved without incurring acoding bit cost, for example, by constructing and adding symmetricbi-pred MVs to merge candidate lists.

The method(s) described herein may be performed by a decoder. In someexamples, the method(s) herein or a corresponding method(s) may beperformed by an encoder. A computer-readable medium may includeinstructions for causing one or more processors to perform the method(s)described herein. A computer program product including instructionswhich, when the program is executed by one or more processors, may causethe one or more processors to carry out the method(s) described herein.

Merge mode MV coding may be performed (e.g., for a coding block (CB) orprediction unit (PU)). For example, an MV of a CB or PU may be encodedin a merge mode or an AMVP mode. In merge mode, coded MVs forneighboring PUs may be collected as MV candidates in a merge candidatelist (e.g. to code a current PU). An index of the candidate list may becoded and transmitted to a decoder. A decoder may reproduce MVcandidates (e.g., based on the coded index), for example, to use for MOPof the PU. In AMVP mode, neighboring coded MVs may be used as MVPs(e.g., in an AMVP candidate list (AMVPCL)). An encoder may encode (e.g.,for a decoder) an index of an MVP list and the (e.g., remaining) MVdifferences (MVDs).

Merge mode may be used for regular translational motion model based MCPand/or for affine motion model based MCP.

Merge mode of regular inter prediction may be performed. In regularinter prediction merge mode (e.g., non-affine translational motion modelbased MCP), a merge candidate list may be constructed, for example, withone or more of the following types of candidates (e.g., in order):spatial MVP from spatial neighbour coding units (CUs), temporal MVP fromcollocated CUs, history-based MVP from a First-In-First-Out (FIFO)table, pairwise average MVP, symmetric bi-pred MV(s), and/or zero MVs.

A size of a merge list may be signaled, for example, in a slice header.A merge list may have a maximum size (e.g., a maximum number ofcandidates). In an example, a maximum size of a merge list may be, forexample, six. Other examples may have a different maximum or may nothave a maximum number of candidates. An index of a best merge candidatemay be encoded, for example, for a (e.g., each) CU coded in merge mode.Different categories of merge candidates may have different generationprocedures.

Merge candidates may include spatial candidates. Spatial mergecandidates may be derived. In an example, (e.g., a maximum of four)merge candidates may be selected among candidates located in thepositions depicted in FIG. 5.

FIG. 5 is a diagram showing example positions of spatial mergecandidates. The order of derivation may be, for example, A₁, B₁, B₀, A₀and B₂. Position B₂ may be considered, for example, when (e.g., onlywhen) CU(s) of position A₁, B₁, B₀, A₀ is/are not used and/or is/areintra coded. CU(s) of position A₁, B₁, B₀, A₀ may not be used, forexample, when the CU(s) of position A₁, B₁, B₀, A₀ belong(s) to anotherslice or tile or is/are otherwise not available. The one or more of theCUs of position A₁, B₁, B₀, AP may not provide an MV candidate for amerge mode of a current CU, for example, if one or more of the CUs ofposition A₁, B₁, B₀, A₀ is available but intra coded. Addition ofcandidates may be subject to a redundancy check, for example, after afirst candidate (e.g., at position A₁) is added. A redundancy check mayremove (e.g., from a merge candidate list) redundant candidates with thesame motion information. A redundancy check may consider selectedcandidate pairs (e.g. to reduce computational complexity).

Merge candidates may include temporal candidates. In an example, a(e.g., only one) temporal MVP (TMVP) candidate may be added to a mergecandidate list. A temporal merge candidate may be derived. A scaledmotion vector (e.g., for a temporal merge candidate) may be derived, forexample, based on co-located CU(s) belonging to a collocated referencepicture. The reference picture list to be used for derivation of theco-located CU may be indicated, for example, by explicit signaling,e.g., in the slice header. A scaled motion vector for a temporal mergecandidate may be obtained, for example, as illustrated by the dottedline in FIG. 6.

FIG. 6 is a diagram showing an example of motion vector scaling for atemporal merge candidate, A scaled motion vector for a temporal mergecandidate may be scaled, for example, based on a motion vector for theco-located CU, e.g., using POC (Picture Order Count) distances, tb andtd. POC distance tb may indicate a POC difference between the currentpicture and a reference picture of the current picture. POC distance tdmay indicate a POC difference between the collocated picture and areference picture of the co-located picture. The reference picture indexof the temporal merge candidate may be set equal to zero. The positionfor the temporal candidate may be selected, for example, betweencollocated center candidate CU and collocated bottom-right CU.

Merge candidates may include history-based merge candidates.History-based MVP (HMVP) merge candidates may be added to a mergecandidate list, for example, after spatial MVP and TMVP mergecandidates. Motion information of a previously coded block may be storedin a table and/or used as MVP for a current CU. A previously coded blockmay or may not be from immediate neighbor CUs. A table with multipleHMVP candidates may be maintained, e.g., during an encoding/decodingprocess. The table may be reset (e.g., by emptying the table), forexample, when a different coding tree unit (CTU) row is (e.g., first)encountered. Motion information may be added to the last entry of thetable (e.g., as a new HMVP candidate), for example, if (e.g., when)there is a non-subblock inter-coded CU (e.g., for an affine mode).

HMVP table size S may be limited to a maximum number of table entries.In an example, HMVP table size S may be set to be 6, which may indicateup to 6 HMVP candidates may be added to the table. A constrainedfirst-in-first-out (FIFO) rule may be utilized, for example, wheninserting a (e.g., new-) motion candidate into the table. A redundancycheck may be applied to determine whether there is an identical HMVP inthe table. An identical (e.g. redundant) HMVP may be removed from thetable. HMVP candidates may be moved forward, for example, when acandidate is removed.

HMVP candidates (e.g., in an HMVP table) may be used in a mergecandidate list construction process. HMVP candidates (e.g., in thetable) may be checked (e.g., in order), for example, to determinewhether an HMVP candidate should become a merge candidate. An HMVPcandidate may be inserted into a merge candidate list, for example,after a TMVP candidate. A redundancy check may be applied, for example,to determine whether the HMVP candidate(s) is/are redundant, e.g.,relative to spatial or temporal merge candidates. In an example, thenumber of redundancy check operations may be reduced.

Merge candidates may include pair-wise average merge candidates.Pairwise average candidates may be generated, for example, by averagingpredefined pairs of candidates in the available (e.g., existing) mergecandidate list. Predefined pairs may be defined as, for example, {(0,1), (0, 2), (1, 2), (0, 3), (1, 3), (2, 3)}. The numbers may denotemerge indices to the merge candidate list. The averaged motion vectorsmay be calculated, for example, separately for each reference list. Two(e.g. both) motion vectors available in one reference list may beaveraged, for example, even when the motion vectors point to differentreference pictures. If only one motion vector is available, the motionvector may be used directly. If no motion vector is available, the listmay be invalid.

Merge candidates may include symmetric bi-pred motion vectors.Construction of symmetric bi-pred motion vectors is discussed in greaterdetail below.

Merge candidates may include zero MVPs, A merge list may not be fullafter symmetric bi-pred MV merge candidates are added to the merge list.Zero MVPs may be inserted at the end of a merge candidate list, forexample, up to a maximum number of merge candidates.

Affine motion compensated prediction may be performed. Block-basedaffine transform motion compensation prediction (MCP) may be applied (asdescribed herein), for example, for many kinds of motion, e.g., zoomin/out, rotation, perspective motions and other irregular motion.

FIG. 7A-C are diagrams showing examples of control point based affinemotion models, including a 4 parameter affine model, a 6 parameteraffine model, and an affine motion vector field (MVF) per sub-block. Anaffine motion field of a block may be described by motion information oftwo control point (e.g., 4-parameter) or three control point (e.g.,6-parameter) motion vectors.

For a 4-parameter affine motion model (e.g., as shown in FIG. 7A), amotion vector at sample location (x, y) in a block may be derived, forexample, using Eq. 1:

$\begin{matrix}\{ \begin{matrix}{{mv}_{x} = {{\frac{{mv}_{1\; x} - {mv}_{0\; x}}{W}x} + {\frac{{mv}_{1y} - {mv}_{0y}}{W}y} + {mv}_{0x}}} \\{{mv}_{y} = {{\frac{{mv}_{1\; y} - {mv}_{0\; y}}{W}x} + {\frac{{mv}_{1y} - {mv}_{0x}}{W}y} + {mv}_{0y}}}\end{matrix}  & {{Eq}.\mspace{11mu} 1}\end{matrix}$

For a 6-parameter affine motion model (e.g., as shown in FIG. 7B), amotion vector at sample location (x, y) in a block may be derived, forexample, using Eq. 2:

$\begin{matrix}\{ \begin{matrix}{{mv}_{x} = {{\frac{{mv}_{1\; x} - {mv}_{0\; x}}{W}x} + {\frac{{mv}_{2x} - {mv}_{0x}}{H}y} + {mv}_{0x}}} \\{{mv}_{y} = {{\frac{{mv}_{1\; y} - {mv}_{0\; y}}{W}x} + {\frac{{mv}_{2y} - {mv}_{0x}}{H}y} + {mv}_{0y}}}\end{matrix}  & {{Eq}.\mspace{11mu} 2}\end{matrix}$

With reference to FIGS. 7A and 7B and to Eq. 1 and Eq. 2, a motionvector of the top-left corner control point v₀ may be indicated by(mv_(0x), mv_(0y)). A motion vector of the top-right corner controlpoint vi may be indicated by (mv_(1x), mv_(1y)). A motion vector of thebottom-left corner control point v₂ may be indicated by (mv_(2x),mv_(2y)).

Block based affine transform prediction may be applied, for example, tosimplify the motion compensation prediction. A motion vector of thecenter sample of a sub-block may be calculated to derive motion vectorfor a 4×4 luma sub-block. In an example, as shown in FIG. 7C, a motionvector of the center sample of each sub-block may be calculatedaccording to Eq. 1 and/or Eq. 2, for example, to derive a motion vectorfor each 4×4 luma sub-block. A motion vector may be rounded to 1/16fraction accuracy. Motion compensation interpolation filters may beapplied, for example, to generate a prediction for a (e.g., each)sub-block with a derived motion vector. The sub-block size ofchroma-components may be set to be 4×4, for example. The MV of a 4×4chroma sub-block may be calculated, for example, as the average of theMVs of four corresponding 4×4 luma sub-blocks.

There may be multiple affine motion inter prediction modes (e.g.,including affine merge mode and affine AMVP mode), for example, similarto multiple modes of translational motion inter prediction.

Merge mode for affine prediction may be performed. Affine merge mode maybe applied for CUs having, for example, width and height larger than orequal to 8. In affine merge mode, control point MVs (CPMVs) for acurrent CU may be generated, for example, based on motion informationfor spatial neighboring CUs. The number of CPMVP candidates may belimited (e.g., to a maximum number of candidates). In an example, theremay be one or more (e.g., up to five) CPMVP candidates. An index may besignaled (e.g., by an encoder to a decoder) to indicate a CPMVPcandidate to be used for a current CU.

An affine merge candidate list may include one or more of the followingtypes of CPMV candidates: inherited affine merge candidates that may beextrapolated from the CPMVs of neighbor CUs, constructed affine mergecandidate CPMVPs that may be derived using the translational MVs ofneighbor CUs, constructed symmetric affine merge candidates that may bederived by a symmetric mapping of existing affine merge candidates,and/or zero MVs.

Inherited affine merge candidates may be used for affine prediction. Thenumber of inherited affine merge candidates may be limited. In anexample, there may be a maximum of two inherited affine candidates in anaffine merge candidate list. Inherited affine candidates may be derived,for example, from an affine motion model of neighboring blocks (e.g.,one from left neighboring uUs and one from above neighboring CUs).

FIGS. 8A and 8B are diagrams showing example locations of inheritedaffine motion predictors and examples of control point motion vectorinheritance. FIG. 8A shows example candidate blocks. In an example(e.g., for the left predictor), the scan order may be A0 to A1. In anexample (e.g., for the above predictor), the scan order may be B0 to B1to B2. In an example, the first inherited candidate from each side maybe selected. Pruning check may not be performed between two inheritedcandidates. In an example (e.g., when a neighboring affine CU isidentified), a neighboring affine CU's control point motion vectors maybe used, for example, to derive the CPMVP candidate in the affine mergelist of the current CU. In an example (e.g., when, as shown in FIG. 8B,a neighbour left bottom block A is coded in an affine mode), motionvectors v₂, v₃ and v₄ (e.g., of the top left corner, above right cornerand left bottom corner of the CU that contains block A) may be attained.Two CPMVs of a current CU may be calculated, e.g., according to v₂, andv₃, for example, if block A is coded with a 4-parameter affine model.Three CPMVs of a current CU may be calculated, e.g., according to v₂, v₃and v₄, for example, if block A is coded with a 6-parameter affinemodel.

Constructed affine candidates may be used for affine prediction. Anaffine candidate may be constructed, for example, by combining neighbortranslational motion information from a (e.g., each) control point.

FIG. 9 is a diagram showing example locations of candidate positions forconstructed affine merge mode. Motion information for the controlpoint(s) may be derived from specified spatial neighbors and temporalneighbors (e.g., shown by example in FIG. 9). CPMV (k-=, 2, 3, 4) mayrepresent the k-th control point. In an example (e.g. for CPMV₁), blocksmay be checked for availability in an order (e.g., block B2 then B3 thenA2). The MV of the first available block may be used. In an example(e.g., for CPMV₂), blocks may be checked for availability in order(e.g., block B1 then B0). In an example (e.g., for CPMV₃), blocks may bechecked for availability in order (e.g., block A1 then A0). TMVP may beused as CPMV₄, for example, if TMVP is available.

Affine merge candidates may be constructed based on the motioninformation, for example, after MVs of the four control points areattained. Combinations of control point MVs may be used to construct thefollowing (e.g., in order):

-   -   {CPMV₁, CPMV₂, CPMV₃}, {CPMV₁, CPMV₂, CPMV₄}, {CPMV₁, CPMV₃,        CPMV₄}, {CPMV₂, CPMV₃, CPMV₄}, {CPMV₁, CPMV₂}, {CPMV₁, CPMV₃}

A combination of three CPMVs may construct a 6-parameter affine mergecandidate. A combination of two CPMVs may construct a 4-parameter affinemerge candidate. The related combination of control point MVs may bediscarded, for example, if reference indices of control points aredifferent, e.g., to avoid a motion scaling process.

Constructed symmetric affine merge candidates may be used for affineprediction. Constructed symmetric affine merge candidates may be derivedby a symmetric mapping of existing affine merge candidates. Constructedsymmetric affine merge candidates are discussed in greater detail below.

Zero MVs may be used for affine prediction. Zero MVs are inserted at theend of an affine merge candidate list, for example, if the list is stillnot full, for example, after other constructed affine merge candidatesare checked, such as constructed symmetric affine merge candidates.

Symmetric MV Difference (MVD) for bi-prediction may be performed. Amotion vector in forward and backward reference pictures may besymmetric, for example, due to the continuity of motion trajectory inbi-directional prediction. Symmetric motion vector difference (MVD) mayinclude an inter coding mode that uses the continuity of motiontrajectory in bi-prediction. In a symmetric MVD mode, the MVD ofreference picture List 1 may be symmetric to the MVD of List 0. In anexample, (e.g., only) the MVD of reference picture List 0 may besignaled (e.g., for coding efficiency). An MV coded for a currentpicture in a symmetric MVD mode may be derived, for example, using Eq.3:

$\begin{matrix}\{ \begin{matrix}{( {{mvx}_{0},{mvy}_{0}} ) = ( {{{mvpx}_{0} + {mvdx}_{0}},{{mvpy}_{0} + {mvdy}_{0}}} )} \\{( {{mvx}_{1},{mvy}_{1}} ) = ( {{{mvpx}_{1} - {mvdx}_{0}},{{mvpy}_{1} - {mvdy}_{0}}} )}\end{matrix}  & {{Eq}.\mspace{11mu} 3}\end{matrix}$

Subscripts in Eq. 3 may indicate reference list 0 or 1 Directions may beindicated by x and y (e.g., x may indicate horizontal direction and ymay indicate vertical direction).

Symmetric MVD mode may be available for bi-prediction, for example, wheneither of the following are true: (i) reference list 0 contains aforward reference picture and reference list 1 contains a backwardreference picture; and/or (ii) reference list 0 contains a backwardreference picture and reference list 1 contains a forward referencepicture.

In an example, reference picture indices of reference list 0 and list 1may not be signaled, e.g. in a symmetric MVD mode. Reference pictureindices of reference list 0 and list 1 may be derived, for example, in asymmetric MVD mode.

In an example (e.g., if reference list 0 contains forward referencepicture and reference list 1 contains backward reference picture), thereference picture index in list 0 may be set to the nearest forwardreference picture relative to the current picture, and the referencepicture index of list 1 may be set to the nearest backward referencepicture relative to the current picture. In an example (e.g., ifreference list 0 contains backward reference picture and reference list1 contains forward reference picture), the reference picture index inlist 0 may be set to the nearest backward reference picture relative tothe current picture, and the reference picture index of list 1 may beset to the nearest forward reference picture relative to the currentpicture.

Symmetric MVD may reduce signaling overhead and/or coding complexity.For example, symmetric MVD mode may avoid signaling a reference pictureindex for both reference picture lists. Symmetric MVD mode may signal(e.g. only) one set of MVD for one list (e.g., list-0).

Decoder side motion vector refinement (DMVR) may be performed.Bilateral-matching (BM) based decoder side motion vector refinement maybe applied, for example, to increase the accuracy of the MVs of a mergemode. A refined MV may be searched around initial MVs in referencepicture list L0 and/or reference picture list L1, for example, in abi-prediction operation. BM may calculate a distortion between twocandidate blocks in reference picture list L0 and list L1.

FIG. 10 is a diagram showing an example of decoding side motion vectorrefinement. As illustrated in FIG. 10, the sum of absolute difference(SAD) between blocks 1004 and 1006 may be calculated, for example, basedon each MV candidate around the initial MV. The MV candidate with thelowest SAD may become the refined MV and/or may be used to generate thebi-predicted signal.

The refined MV (e.g., derived by the DMVR process) may be used togenerate inter prediction samples and/or may be used in temporal motionvector prediction for future picture coding. The original MV may be usedin a deblocking process and/or may be used in spatial motion vectorprediction for future CU coding.

As shown in FIG. 10, search points may surround the initial MV. The MVoffset may obey an MV difference mirroring (e.g., symmetric) rule.Points may be checked by DMVR (e.g., denoted by candidate MV pair (MV0,MV1)), for example, in accordance with Eq.4:

$\begin{matrix}\{ \begin{matrix}{{{MV}\; 0^{\prime}} = {{{MV}\; 0} + {MV}_{offset}}} \\{{{MV}\; 1^{\prime}} = {{{MV}\; 1} - {MV}_{offset}}}\end{matrix}  & {{Eq}.\mspace{11mu} 4}\end{matrix}$

MV_(offset) may represent the refinement offset between the initial MVand the refined MV, for example, in one of the reference pictures. Therefinement search range may be, for example, two integer luma samplesfrom the initial MV. Search complexity may be reduced, for example, byusing a fast searching method with an early termination trigger.

A merge mode MV candidate may be constructed for PUs (e.g., that allowbi-directional prediction). A merge mode MV candidate may beconstructed, for example, via symmetric mapping of a (e.g., an existing)candidate MV from one direction to the other (e.g., from a forwardreference picture to a backward reference picture and vice versa). Mergemode MV candidates may be constructed, for example, for a regular interprediction merge mode and/or for an affine prediction merge mode.Symmetric MV candidate construction via symmetric mapping may be used,for example, if motion trajectory is continuous across video frames.

FIG. 11A is a diagram showing an example of symmetric merge MV candidateconstruction for regular motion. In an example (e.g., as shown in FIG.11), an original merge MV candidate (MVx0, MVy0) may use a referencepicture in List 0. A symmetric mapping of the MV in the other referencepicture List 1 may be derived from original merge MV candidate (MVx0,MVy0). The derived symmetric mapped MV candidate may be (MVx1, MVy1).The MVs in short arrowed lines may indicate MV magnitudes. The longarrowed lines may illustrate a motion trajectory across pictures (e.g.,from a reference picture in List 0 to the current picture and/or fromthe current picture to a reference picture in List 1). τ₀, τ₁ mayrepresent temporal distances (e.g. POC distances) between the referencepicture in List 0 and the current picture or between the current pictureand the reference picture in List 1, respectively. In an example,translational motion vectors MV0 and MV1 (e.g., short arrowed lines) mayindicate symmetric translational motion vectors (e.g., MV1=−MV0).

A symmetric merge candidate may be constructed (e.g., from availablemerge candidates) for regular inter prediction. A merge candidate list(e.g., an available merge candidate list) may include, for example, oneor more of the following: spatial neighbor MVs, temporal collocated MV,history MVPs, pair-wise average MVs, and/or zero MVs. In examples, oneor more symmetric bi-prediction MV candidate(s) may be located, forexample, (i) after non-zero MVs and before zero MVs, (ii) after historyMVPs and before pair-wise average MVs, and/or (iii) after temporalcollocated MVs and before history MVPs. The location of the symmetricbi-prediction MV candidate(s) may depend on, for example, testingresults. Locating right before zero MVs may be the most conservativelocation.

A symmetric merge candidate may be used for PUs that allowbi-directional prediction. For example, a symmetric merge candidate maybe used for PUs for a tile group type B and/or for a B-slice/picture.

Symmetric MV candidates may be constructed and/or added based onavailable (e.g., existing) merge candidates. Example procedures areprovided herein to construct symmetric merge MV candidates (e.g. basedon available merge candidates).

In an example, the following example procedure may be implemented for an(e.g. each) available (e.g., existing) merge MV candidate (e.g.,candidate i), in the merge candidate list (e.g., starting from the topcandidate).

An existing merge mode MV candidate in a merge candidate list (e.g.,merge candidate i) may have horizontal MvCand_(i,x) verticalMvCand_(i,y), prediction reference picture list refPicList, referencepicture index refPickdx, and reference picture order count (POC)refPicPoc. A determination may be made whether candidate i is auni-directional prediction (uni-pred) MV. A determination may be made(e.g., for uni-pred MV i) whether there is a reference picture in adifferent reference picture list (e.g., List (1−refPlcList)) that is ofequal POC distance to the current picture, the current tile group, orthe current slice as the POC distance between the current picture andthe picture list referenced by merge candidate i. For example, adetermination may be made whether Eq. 5 is true:

refPicPocCand_(i)−curPicPoc==curPicPoc−refPicPocSym_(j)  Eq. 5

where refPicPocCand_(i), curPicPoc, and refPicPocSym; may, respectively,denote a POC of the reference picture of the merge candidate i (e.g.,candidate being evaluated), POC of the current picture/slice, and POC ofa reference picture from the different reference picture list (e.g.,List (1−refPicList)).

A symmetric bi-pred MV candidate may be created for merge candidate i,for example, if a symmetric reference picture with an equivalent POC isincluded in the different reference picture list (e.g., List(1−refPicList)). Uni-pred merge candidate MV (e.g. merge candidate i)may be denoted refPicList MV. A symmetric bi-pred MV candidate derivedfrom refPicList MV may be denoted (1−refPicList) MV), indicating an MVin a prediction direction opposing the prediction direction of theuni-pred merge candidate MV. The symmetric bi-pred MV candidate may bederived as the symmetrical mapping of the uni-pred candidate MV, forexample, using Eq. 6:

MtSym_(i,x)=−MvCand_(i,x),MvSym_(i,y)=−MvCand_(i,y)  Eq. 6

A symmetric MV candidate may be added to the merge candidate list, forexample, based on one or more factors/criterion, such as one or moreconditions. In an example, a symmetric bi-pred merge candidate may beadded to a merge candidate list if: (i) the symmetric bi-pred candidate(e.g., the newly created symmetric bi-pred candidate) is not already inthe merge candidate list (e.g. is not redundant with a candidate in thelist); (ii) the total number of available merge candidates in the listis less than the maximum number of allowed merge candidates; and (iii)the total number of symmetric merge candidates in the merge candidatelist is less than the maximum number of allowed symmetric mergecandidates. In an example, the symmetric MV candidate may not be addedto the merge candidate list, for example, if (i), (ii) or (ii) in theexample is not true.

A determination may be made whether candidate i is a bi-pred MV. In anexample (e.g., where candidate i is a bi-pred MV), candidate i's MVsassociated with the two reference picture lists may be consideredseparately as two individual (e.g., different) uni-pred candidates. Theforegoing symmetric mapping procedure to construct a symmetric bi-predcandidate (e.g., and conditionally add to the merge candidate list) maybe applied to each of the two individual uni-pred candidates. Thesymmetric mapping applied to the two individual uni-pred candidates maygenerate two different symmetric merge candidates. A determinationwhether to add the two different symmetric merge candidates may be madeindividually, e.g., applied separately to each symmetric mergecandidate.

The procedure checking each existing merge candidate in the mergecandidate list may stop, for example, if the maximum number of neededmerge candidates and/or the maximum number of allowed symmetric mergecandidates is reached. The next available candidate in the list may beprocessed, e.g., as candidate i is processed as described herein, forexample, if the maximum number of needed merge candidates and themaximum number of allowed symmetric merge candidates are not reached.Added (e.g., newly added) symmetric candidates may not be processed, forexample, as candidate i is processed as described herein.

In examples, the symmetric reference picture (e.g., equal POC distance)condition may be based on the closest POC distance, for example, ratherthan an equivalent POC distance, to consider whether to generate asymmetric bi-pred merge candidate for an existing merge candidate. In anexample, a closest POC may comprise an equivalent POC (e.g., with zerodifference). Whether a conditional POC distance is equal, closest to oranother distance may depend on, for example, testing results. In anexample, checking the different (e.g., the other) reference list asdescribed herein may comprise searching for a reference picture thatprovides the closest POC distance to the current picture to meet theequal POC distance condition (e.g., as provided in Eq. 5). For example,Eq. 7 may be used:

$\begin{matrix}{\min\limits_{j}( {{{refPicPocSym}_{j} - ( {{2 \cdot {curPicPoc}} - {refPicPocCand}_{i}} )}} )} & {{Eq}.\mspace{11mu} 7}\end{matrix}$

In examples (e.g., where Eq. 7 is used), MV scaling may be applied, forexample, based on different POC distances to the current picture. In anexample, MV scaling may be applied in accordance with Eq. 8:

$\begin{matrix}{{{{MvSym}_{i,x} = {{- \alpha} \cdot {MvCand}_{i,x}}},{{MvSym}_{i,y} = {{- \alpha} \cdot {MvCand}_{i,y}}}}{\alpha = \frac{{curPicPoc} - {refPicPocSym}_{j}}{{refPicPocCand}_{i} - {currPicPoc}}}} & {{Eq}.\mspace{11mu} 8}\end{matrix}$

In examples, the maximum number of allowed symmetric merge candidatesmay be set to 1 or 2, for example. A maximum number of symmetric mergecandidates may depend on, for example, testing results.

In examples, symmetric merge candidate construction (e.g., as describedherein) may be applied, for example, to available (e.g., existing)uni-pred merge candidates (e.g., only to existing uni-pred mergecandidates), bi-pred merge candidates (e.g., only to existing bi-predmerge candidates), and/or uni-pred or bi-pred merge candidates (e.g., toall existing candidates regardless whether uni-pred or bi-pred).

A symmetric merge candidate construction (e.g., as described herein) maybe conducted at the encoder and/or the decoder sides. The decoder mayproduce the same symmetric merge candidate constructed by an encoder,for example, based on a coded merge candidate index of a merge candidatelist. Adding a symmetric merge candidate to a merge candidate list(e.g., as described herein) may improve coding efficiency withoutincurring extra coding and/or signaling costs.

A coding syntax on merge mode (e.g., merge_flag and merge_index, etc.)may be provided. Merge candidate list construction may be provided. Inan example, a symmetric merge candidate may be added in one or morelocations in a merge candidate list (e.g., between a pair-wise averageMV candidate and a zero MV candidate).

A symmetric merge candidate may be constructed for affine modeprediction. A merge mode may be used for affine mode MCP, for example. Amerge candidate list for affine mode may be different from a mergecandidate list for a regular inter prediction. In an example of affinemode prediction, merge candidates may include, for example, one or moreof inherited affine merge candidates, constructed affine mergecandidates, and/or zero MVs. Symmetric affine merge candidates (e.g.,created as described herein) may be located, for example, after non-zeroMV candidates and before zero MV candidates in an affine merge candidatelist.

FIG. 11B is a diagram showing an example of symmetric merge MV candidateconstruction for affine motion. In an example (e.g., as shown in FIG.11B), original affine merge candidate (e.g. with multiple MVs) may use areference picture in List 0. A symmetric mapping of the MVs in the otherreference picture List 1 may be derived from original affine mergecandidate MVs. In an example, translational motion vectors MV0 and MV1(e.g., short arrowed lines) may indicate symmetric translational motionvectors (e.g., MV1=−MV0). Rotational arrows denoted with rotation anglesθ₀ and θ₁ may indicate symmetric rotation motion vectors (e.g., θ₁=−θ₀).Zooming factors ρ0 and ρ1 may indicate symmetric zooming motion vectors(e.g., ρ1=1/ρ0). Similar to FIG. 11A, τ₀, τ₁ may represent temporaldistances (e.g. POC distances) between the reference picture in List 0and the current picture or between the current picture and the referencepicture in List 1, respectively.

In examples, symmetric affine merge candidate construction may besimilar to symmetric merge candidate construction for regular interprediction. Differences between symmetric mapping for affine mode andregular inter prediction are described below.

In an affine mode (e.g., for a uni-pred candidate), two or three controlpoint MVs may be used for a 4-parameter and 6-parameter affine model,respectively (e.g., as described herein). A symmetric mapping (e.g., asshown in Eq. 6) may be applied for the top-left control point MV. Thetop-left control point MV may represent the same translational motion asan MV in regular inter prediction mode. MVs for the other (e.g., 1 or 2)control points (e.g., top-right and bottom-left control points) mayfollow different symmetric mapping calculations. MVs for the other 1 or2 control points (e.g., top-right and bottom-left control points) mayrepresent zooming and/or rotational motion information.

An affine motion model may be expressed, for example, in accordance withEq. 9:

$\begin{matrix}\{ \begin{matrix}{{MV}_{({x,y})}^{h} - {( {{\rho_{x}\cos\;\theta_{x}} - 1} ) \cdot x} + {\rho_{y}\sin\;{\theta_{y} \cdot y}} + d_{x}} \\{{MV}_{({x,y})}^{v} - {\rho_{x}\sin\;{\theta_{x} \cdot x}} + {( {{\rho_{y}\cos\;\theta_{y}} - 1} ) \cdot y} + d_{y}}\end{matrix}  & {{Eq}.\mspace{11mu} 9}\end{matrix}$

As shown in Eq. 9, MV_((x,y)) ^(h) and MV_((x,y)) ^(v) may be thehorizontal and vertical components of an MV at position (x, y), d_(x),d_(y) may be spatial translations, ρ_(x), ρ_(y) may be zooming factors,θ_(x), θ_(y) may be rotation angles, and x, y may represent horizontaland vertical directions, respectively. Eq. 9 may represent a 6-parameteraffine motion model. A4-parameter affine model may be consistent withEq. 9 and Eq. 10:

ρ_(x)=ρ_(y),θ_(x)=θ_(y)  Eq. 10

For example, in a 4-parameter affine model, MVs of two control pointsmay be known from a uni-pred MV candidate. MVs of the two control pointsmay include, for example, (mv_(x) ⁰, mv_(y) ⁰) of the top-left controlpoint 0 at (0,0) and (mv_(x) ¹,mv_(y) ¹) of the top-right control pointat (w, 0), where w may be the width of the CU. Plugging in the twopositions and MVs into Eq. 9, combined with the assumption of Eq. 10,may yield Eq. 11 and Eq. 12:

$\begin{matrix}{{{mv}_{x}^{0} = d_{x}},{{mv}_{y}^{0} = {d_{y}.}}} & {{Eq}.\mspace{11mu} 11} \\\{ \begin{matrix}{{mv}_{x}^{1} = {{( {{\rho\;\cos\;\theta} - 1} ) \cdot w} + d_{x}}} \\{{mv}_{y}^{1} = {{{- \rho}\;\sin\;{\theta \cdot w}} + d_{y}}}\end{matrix}  & {{Eq}.\mspace{11mu} 12}\end{matrix}$

Plugging Eq. 11 into Eq. 12 may yield Eq. 13:

$\begin{matrix}\{ \begin{matrix}{{{\rho \cdot \sin}\;\theta} = \frac{{mv}_{y}^{1} - {mv}_{y}^{0}}{w}} \\{{{\rho \cdot \cos}\;\theta} = {\frac{{mv}_{x}^{1} - {mv}_{x}^{0}}{w} + 1}}\end{matrix}  & {{Eq}.\mspace{11mu} 13}\end{matrix}$

In examples, look-up-tables (LUTs) may be used to solve for the valuesof one or more of ρ, sin θ, and/or cos θ, for example, so that theencoder and the decoder may yield the same calculation results. In anexample, LUTs may be created using Eq. 14:

$\begin{matrix}{{{{{tab}_{1}\lbrack a\rbrack} = \lbrack {N \cdot {\sin( \;{a\mspace{11mu}{\tan( \frac{a}{N} )}} )}} \rbrack},{{{tab}_{2}\lbrack b\rbrack} = \lbrack {N \cdot {\sin( {a\;{\tan( \frac{N}{b} )}} )}} \rbrack}}{{{{tab}_{3}\lbrack a\rbrack} = \lbrack {N \cdot {\cos( \;{a\mspace{11mu}{\tan( \frac{a}{N} )}} )}} \rbrack},{{{tab}_{4}\lbrack b\rbrack} = \lbrack {N \cdot {\cos( {a\;{\tan( \frac{N}{b} )}} )}} \rbrack}}} & {{Eq}.\mspace{11mu} 14}\end{matrix}$

As shown in Eq. 14, N may control precision (e.g., 256), a=0 . . . N,and b=1 . . . N. The square bracket used on the right side of Eq. 14 maydenote rounding to the nearest integer.

In an example of symmetric mapping to a different prediction direction(e.g., the opposite prediction direction), the four affine modelparameters (d_(x), d_(y), ρ, θ) may be (e.g., symmetrically) mapped to(−d_(x), −d_(y), ρ⁻¹, −θ). Symmetric mapping of the zooming ratio ρ maybe the inverse of zooming ratio ρ (e.g. with reference to FIG. 11,ρ1=1/ρ0). The corresponding two control point MVs of the other referencepicture list (e.g., from the opposite prediction direction) may bederived, for example, as shown in Eq. 15 and Eq. 16:

$\begin{matrix}{{{mvSym}_{x}^{0} = {- d_{x}}},{{mvSym}_{y}^{0} = {- {d_{y}.}}}} & {{Eq}.\mspace{11mu} 15} \\\{ \begin{matrix}{{mvSym}_{x}^{1} = {{( {{\rho^{- 1}\;\cos\;\theta} - 1} ) \cdot w} + d_{x}}} \\{{mvSym}_{y}^{1} = {{\rho^{- 1}\;\sin\;{\theta \cdot w}} - d_{y}}}\end{matrix}  & {{Eq}.\mspace{11mu} 16}\end{matrix}$

As shown in Eq. 15 and Eq. 16, (mvSym_(x) ⁰, mvSym_(y) ⁰), (mvSym_(x) ¹,mvSym_(y) ¹) may denote the two control point Ms from the otherprediction direction.

Starting from Eq. 9, a 4-parameter affine motion symmetric mappingprocess (e.g., as described herein) may be extended to solve and/orcalculate the symmetric mapping MV of the bottom left control point fora 6-parameter affine model. In Eq. 17, (mv_(x) ², mv_(y) ²) may denotethe bottom-left control point at (0, h), where h is the height of theCU.

Plugging (0, h) into Eq. 9 may yield Eq. 17:

$\begin{matrix}\{ \begin{matrix}{{mv}_{x}^{2} = {{\rho_{y}\;\sin\;{\theta_{y} \cdot h}} + d_{x}}} \\{{mv}_{y}^{2} = {{( {{\rho_{y}\;\cos\;\theta_{y}} - 1} ) \cdot h} + d_{y}}}\end{matrix}  & {{Eq}.\mspace{11mu} 17}\end{matrix}$

In the 6-parameter affine model of Eq. 9, the herein derived (ρ, 0) inthe 4-parameter model may become (ρ_(x), θ_(x)), which denotes thezooming ratio and rotation angle along the horizontal direction. Eq. 17may be used to solve for (ρ_(y), θ_(y)), which denotes the zooming ratioand rotation angle along the vertical direction.

Plugging Eq. 11 into Eq. 17 may yield Eq. 18:

$\begin{matrix}\{ \begin{matrix}{{{\rho_{y} \cdot \sin}\;\theta_{y}} = \frac{{mv}_{x}^{2} - {mv}_{x}^{0}}{h}} \\{{{\rho_{y} \cdot \cos}\;\theta_{y}} = {\frac{{mv}_{y}^{2} - {mv}_{y}^{0}}{h} + 1}}\end{matrix}  & {{Eq}.\mspace{11mu} 18}\end{matrix}$

One or more LUTs (e.g., similarly applied as LUTs described herein) maybe used to solve for one or more of ρ_(y), sin θ_(y), and/or cos θ_(y).Symmetric mapping (e.g. using one or more LUTs) may be conducted onmodel parameters to map from (d_(x), d_(y), ρ_(y), θ_(y)) to (d_(x),−d_(y), ρ_(y) ⁻¹, −θ_(y)). The bottom-left control point MV from theother direction may be provided, for example, by Eq. 19:

$\begin{matrix}\{ \begin{matrix}{{mvSym}_{x}^{2} = {{{- \rho_{y}^{- 1}}\;\sin\;{\theta_{y} \cdot h}} - d_{x}}} \\{{mvSym}_{y}^{2} = {{( {{\rho_{y}^{- 1}\;\cos\;\theta_{y}} - 1} ) \cdot h} - d_{y}}}\end{matrix}  & {{Eq}.\mspace{11mu} 18}\end{matrix}$

As presented by way of example herein, different symmetric mappingschemes may be used to construct symmetric affine merge candidates andsymmetric merge candidates for regular inter prediction. One or moreaspects, considerations or variations used for symmetric affine mergecandidate construction and symmetric merge candidate construction forregular inter prediction may be similar (e.g., the same). Similarconsiderations or variations may include, for example, one or more ofthe following: an equal (e.g., or nearest) POC distance condition, amaximum number of allowed symmetric merge candidates, types of existingmerge candidates that may be used (e.g., uni-pred only, bi-pred only, orboth) to construct symmetric candidates, etc.

A coding syntax on affine merge mode (e.g., merge_subblock_flag andrerge_subblock_index) may be provided. Affine merge candidate listconstruction may be provided. Symmetric affine merge candidates (e.g.,as described herein) may be added to an affine merge candidate list, forexample, after a constructed affine merge MV candidate and before a zeroMV candidate).

Although features and elements are described above in particularcombinations, one of ordinary skill in the art will appreciate that eachfeature or element can be used alone or in any combination with theother features and elements. In addition, the methods described hereinmay be implemented in a computer program, software, or firmwareincorporated in a computer-readable medium for execution by a computeror processor. Examples of computer-readable media include electronicsignals (transmitted over wired or wireless connections) andcomputer-readable storage media. Examples of computer-readable storagemedia include, but are not limited to, a read only memory (ROM), arandom access memory (RAM), a register, cache memory, semiconductormemory devices, magnetic media such as internal hard disks and removabledisks, magneto-optical media, and optical media such as CD-ROM disks,and digital versatile disks (DVDs). A processor in association withsoftware may be used to implement a radio frequency transceiver for usein a WTRU, UE, terminal, base station, RNC, or any host computer.

1. A device comprising: a processor configured to: obtain, for aprediction unit (PU) in a current picture, a merge candidate list havinga first merge candidate comprising a first motion vector (MV) associatedwith a first reference picture; obtain a symmetric merge candidate basedon the first merge candidate, the symmetric merge candidate comprisingthe first MV and a second MV symmetric to the first MV, wherein thesecond MV symmetric to the first MV is associated with a secondreference picture; and generate an updated merge candidate list forpredicting an MV for the PU by inserting the symmetric merge candidateinto the merge candidate list for, wherein the updated merge candidatelist comprises the first merge candidate and the symmetric mergecandidate.
 2. A method comprising: obtaining, for a prediction unit (PU)in a current picture, a merge candidate list; obtaining, from the mergecandidate list, a first merge candidate comprising a first motion vector(MV) associated with a first reference picture; obtaining a symmetricmerge candidate based on the first merge candidate, the symmetric mergecandidate comprising the first MV and a second MV symmetric to the firstMV, wherein the second MV symmetric to the first MV is associated with asecond reference picture; generating an updated merge candidate list forpredicting an MV for the PU by inserting the symmetric merge candidateinto the merge candidate list, wherein the updated merge candidate listcomprises the first merge candidate and the symmetric merge candidate;and predicting the MV for the PU based on the updated merge candidatelist.
 3. The device of claim 1, wherein the first reference picture andthe second reference picture have a same picture order count (POC)distance, in opposite directions, to the current picture.
 4. The deviceof claim 1, wherein the processor is further configured to: determinewhether a reference picture list comprises a symmetric reference picturehaving a picture order count (POC) distance to the current picture equalto a POC distance between the first reference picture and the currentpicture; and select the first merge candidate to derive the symmetricmerge candidate based on a determination that the reference picture listcomprises the symmetric reference picture.
 5. The device of claim 1,wherein the first reference picture is in a first reference picturelist, and the processor is further configured to: determine whether asymmetric reference picture having a picture order count (POC) distanceto the current picture equal to a POC distance between the firstreference picture and the current picture exists in a second referencepicture list; and when the symmetric reference picture does not exist inthe second reference picture list, select, in the second referencepicture list, a reference picture having a POC distance to the currentpicture closest to the POC distance between the first reference pictureand the current picture as the second reference picture.
 6. The deviceof claim 1, wherein the processor is further configured to: apply motionvector scaling to construct the second MV for the symmetric mergecandidate based on a picture order count (POC) distance between thesecond reference picture and the current picture, and a POC distancebetween the first reference picture and the current picture.
 7. Thedevice of claim 1, wherein the first merge candidate is auni-directional prediction merge candidate or a bi-directionalprediction merge candidate.
 8. The device of claim 1 wherein theprocessor is further configured to: determine that the first mergecandidate is a bi-directional prediction candidate having the first MVassociated with the first reference picture in a first reference picturelist and a third MV associated with a third reference picture in asecond reference picture list; and obtaining a fourth MV symmetric tothe third MV and associated with a fourth reference picture in the firstreference picture list, where a picture order count (POC) distancebetween the fourth reference picture and the current picture is equal orsimilar to a POC distance between the third reference picture and thecurrent picture, wherein a second symmetric merge candidate of the firstmerge candidate comprises the third MV and the fourth MV associated withthe fourth reference picture in the first reference picture list.
 9. Thedevice of claim 1, wherein the processor is further configured to:determine, before inserting the symmetric merge candidate to the mergecandidate list, that the symmetric merge candidate is not redundant withany other merge candidate in the merge candidate list.
 10. The device ofclaim 1, wherein the processor is further configured to: determine,before inserting the symmetric merge candidate to the merge candidatelist, that inserting the symmetric merge candidate to the mergecandidate list will not exceed at least one of a number of allowed mergecandidates and a number of allowed symmetric merge candidates. 11.(canceled)
 12. The device of claim 1, wherein the first merge candidateand the symmetric merge candidate are affine merge candidates.
 13. Thedevice of claim 1, wherein the first MV of the first merge candidate isa candidate control point MV (CPMV) having x and y spatial translations,a zooming factor and a rotation angle, and the processor is furtherconfigured to: perform symmetric mapping of the CPMV to determinesymmetric affine parameters that comprises negative x and y spatialtranslations, an inverse zooming factor and a negative rotation anglehaving the same magnitude as the rotation angle; and generate asymmetric CPMV of the symmetric merge candidate based on the symmetricaffine parameters.
 14. (canceled)
 15. The device of claim 1, wherein thedevice comprises a decoding device or an encoding device.
 16. (canceled)17. (canceled)
 18. The method of claim 2, further comprising:determining whether a reference picture list comprises a symmetricreference picture having a picture order count (POC) distance to thecurrent picture equal to a POC distance between the first referencepicture and the current picture; and selecting the first merge candidateto derive the symmetric merge candidate based on a determination thatthe-reference picture list comprises the symmetric reference picture.19. The method of claim 2, wherein the first reference picture is in afirst reference picture list, and the method further comprises:determining whether a symmetric reference picture having a picture ordercount (POC) distance to the current picture equal to a POC distancebetween the first reference picture and the current picture exists in asecond reference picture list; and when the symmetric reference picturedoes not exist in the second reference picture list, selecting, in thesecond reference picture list, a reference picture having a POC distanceto the current picture closest to the POC distance between the firstreference picture and the current picture as the second referencepicture.
 20. The method of claim 2, wherein the method furthercomprises: applying motion vector scaling to construct the second MV forthe symmetric merge candidate based on a picture order count (POC)distance between the second reference picture and the current picture,and a POC distance between the first reference picture and the currentpicture.
 21. The method of claim 2, wherein the method is performed by adecoder or an encoder.
 22. The device of claim 1, wherein the devicefurther comprises a memory.
 23. The device of claim 1, wherein thedevice further comprises at least one of (i) an antenna configured toreceive a signal, the signal including data representative of an image,(ii) a band limiter configured to limit the received signal to a band offrequencies that includes the data representative of the image, or (iii)a display configured to display the image.
 24. A computer readablemedium including instructions for causing one or more processors toperform a method of: obtaining, for a prediction unit (PU) in a currentpicture, a merge candidate list; obtaining, from the merge candidatelist, a first merge candidate comprising a first motion vector (MV)associated with a first reference picture; obtaining a symmetric mergecandidate based on the first merge candidate, the symmetric mergecandidate comprising the first MV and a second MV symmetric to the firstMV, wherein the second MV symmetric to the first MV is associated with asecond reference picture; generating an updated merge candidate list forpredicting an MV for the PU by inserting the symmetric merge candidateinto the merge candidate list, wherein the updated merge candidate listcomprises the first merge candidate and the symmetric merge candidate;and predicting the MV for the PU based on the updated merge candidatelist.